eBay:iPads:: sold classification:: txtterms
bdanalytics
Date: (Wed) Jul 22, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "txtterms_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "sold"
# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
# return(log(raw))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))## [1] Y Y N N <NA>
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
# return(exp(var))
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))## [1] 1 1 0 0 NAif ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
glb_id_var <- c("UniqueID")
glb_category_vars <- c("prdline.my")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_derive_lst[["idseq.my"]] <- list(
mapfn=function(UniqueID) { return(UniqueID - 10000) }
, args=c("UniqueID"))
glb_derive_lst[["prdline.my"]] <- list(
mapfn=function(productline) { return(productline) }
, args=c("productline"))
glb_derive_lst[["startprice.log"]] <- list(
mapfn=function(startprice) { return(log(startprice)) }
, args=c("startprice"))
# glb_derive_lst[["startprice.log.zval"]] <- list(
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
mod_raw <- gsub("(\\w)(\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" conditon|condtion|conditions", " condition", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" drop ", " dropped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCL0UD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" shipped ", " ship ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPACOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("descr.my")
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
glb_append_stop_words[["descr.my"]] <- c(NULL
# freq = 1
,"511","825","975"
,"a1314","a1430","a1432"
,"abused","across","adaptor","add","antenna","area","arizona"
,"beginning","bidder","bound"
,"changed","chrome","contents","control","cream"
,"decent","defender","disclaimer","distressed","divider"
,"dlxnqat9g5wt","done","dont","dust","duty"
,"erased","ereader","exact"
,"film","flickers"
,"generic","genuine","glitter"
,"hdmi","higher","hospital"
,"impact","instead"
,"jack","july"
,"keeps"
,"letters","limited","lining","liquid","local","looping","loss"
,"mb292ll","mc707ll","mc916ll","md789ll","mf432ll","mgye2ll"
,"mind","mixed"
,"neither","november"
,"occasional"
,"pet","plug","poor","provided"
,"recently","red","result","running"
,"seem","semi","serious","shell","short","size","slice","smoke"
,"softer","software","somewhat","soon"
,"sparingly","sparkiling","special","speck","speed"
,"stains","standup","status","stopped","subtle","sustained","swappacom"
,"technical","therefore","though","totally","touchy"
,"university","unknown","untouched","upgrade"
,"valid","vary","version"
,"want","website","winning","wrapped"
,"zaag","zombie"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_filter_txt_terms <- "top" # or "sparse"
glb_top_n <- c(10)
names(glb_top_n) <- glb_txt_vars
glb_sprs_thresholds <- c(0.950) # Generates 10 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("productline", "startprice", "description")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # or TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "glmnet", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "glmnet", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL #"Low.cor.X.glm"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 8.32 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 8.320 8.701 0.381
## 2 inspect.data 2 0 8.702 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Loading required package: reshape2
## sold.0 sold.1 sold.NA
## Test NA NA 798
## Train 1001 860 NA
## sold.0 sold.1 sold.NA
## Test NA NA 1
## Train 0.5378829 0.4621171 NA
## [1] "numeric data missing in glb_allobs_df: "
## sold
## 798
## [1] "numeric data w/ 0s in glb_allobs_df: "
## biddable sold
## 1446 1001
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## sold sold.fctr .n
## 1 0 N 1001
## 2 1 Y 860
## 3 NA <NA> 798## Warning: Removed 1 rows containing missing values (position_stack).
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 798
## Train 1001 860 NA
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 1
## Train 0.5378829 0.4621171 NA# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 8.702 12.646 3.944
## 3 scrub.data 2 1 12.647 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in glb_allobs_df: "
## biddable sold
## 1446 1001
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0findOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
sel_obs <- function(vars_lst, ignore.case=TRUE, perl=FALSE) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
# if (!is.null(Popular)) {
# if (is.na(Popular))
# tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
# tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
# }
# if (!is.null(NewsDesk))
# tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
for (var in names(vars_lst)) {
if (grepl(".contains", var))
tmp_df <- tmp_df[grep(vars_lst[var],
tmp_df[, unlist(strsplit(var, ".contains"))],
ignore.case=ignore.case, perl=perl), ]
else
tmp_df <- tmp_df[tmp_df[, var] == vars_lst[var], ]
}
return(glb_allobs_df[, glb_id_var] %in% tmp_df[, glb_id_var])
}
#print(glb_allobs_df[sel_obs(list(description.contains="mini(?!m)"), perl=TRUE), "description"])
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
c(glb_id_var, glb_rsp_var, glb_category_vars, glb_txt_vars, cols),
FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE)
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1595 0 0 0 0 0
## 1 288 0 4 36 27 172 196
## Unknown 4 4 2 0 0 331 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1599 0 0 0 0 0
## 1 292 0 6 36 27 172 196
## Unknown 0 0 0 0 0 331 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 12.647 13.347 0.7
## 4 transform.data 2 2 13.348 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: idseq.my..."
## [1] "Creating new feature: prdline.my..."
## [1] "Creating new feature: startprice.log..."
## [1] "Creating new feature: descr.my..."#stop(here")
hex_vctr <- c("\211", "\235", "\317", "\333")
for (row_pos in c(1904, 2157)) {
tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
collapse="")
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 13.348 13.792 0.444
## 5 extract.features 3 0 13.792 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 13.8 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 13.800 13.815
## 2 extract.features_factorize.str.vars 2 0 13.816 NA
## elapsed
## 1 0.015
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src prdline.my descr.my
## "storage" "productline" ".src" "prdline.my" "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: prdline.my: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_DTM_terms <- function(DTM) {
TfIdf_mtrx <- as.matrix(DTM)
rownames(TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
TfIdf_vctr <- colSums(TfIdf_mtrx)
names(TfIdf_vctr) <- dimnames(DTM)[[2]]
TfIdf_df <- as.data.frame(TfIdf_vctr)
names(TfIdf_df) <- "TfIdf"
TfIdf_df$term <- rownames(TfIdf_df)
TfIdf_df$freq <- colSums(TfIdf_mtrx != 0)
TfIdf_df$pos <- 1:nrow(TfIdf_df)
return(TfIdf_df <- orderBy(~ -TfIdf, TfIdf_df))
}
get_corpus_terms <- function(txt_corpus) {
TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
return(TfIdf_df <- get_DTM_terms(TfIdf_DTM))
}
#stop(here")
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
#tolower Not needed as of version 0.6.2 ?
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
#stop(here")
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_TfIdf_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n TfIDf terms for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.n.post.stop <- rowSums(TfIdf_mtrx > 0)
tmp_allobs_df$TfIdf.sum.post.stop <- rowSums(TfIdf_mtrx)
glb_corpus_lst[[txt_var]] <- tm_map(glb_corpus_lst[[txt_var]], stemDocument,
"english", lazy=TRUE) #Features ???
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
TfIdf_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
tmp_allobs_df$terms.n.post.stem <- rowSums(TfIdf_stem_mtrx > 0)
tmp_allobs_df$TfIdf.sum.post.stem <- rowSums(TfIdf_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.n.post.stem / tmp_allobs_df$terms.n.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio),
"terms.n.stem.stop.Ratio"] <- 1.0
tmp_allobs_df$TfIdf.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$TfIdf.sum.post.stem / tmp_allobs_df$TfIdf.sum.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$TfIdf.sum.stem.stop.Ratio),
"TfIdf.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_df <- get_DTM_terms(full_TfIdf_DTM)
full_TfIdf_df <- full_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(full_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".full", sep="")
names(full_TfIdf_df) <- col_names
# full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
# rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
# full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
# names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
# full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
# names(full_TfIdf_df) <- "TfIdf.full"
# full_TfIdf_df$term <- rownames(full_TfIdf_df)
# full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
# full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_df <- get_DTM_terms(sprs_TfIdf_DTM)
sprs_TfIdf_df <- sprs_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(sprs_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".sprs", sep="")
names(sprs_TfIdf_df) <- col_names
# sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
# names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
# sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
# names(sprs_TfIdf_df) <- "TfIdf.sprs"
# sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
# sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
# sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_DTM_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
if (glb_filter_txt_terms == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_filter_txt_terms == "top") {
txt_X_df <- txt_full_X_df[, terms_full_df$pos[1:glb_top_n[[txt_var]]], FALSE]
} else stop("glb_filter_txt_terms should be one of c('sparse', 'top') vs. '",
glb_filter_txt_terms, "'")
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 13.816 14.077
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## [1] "Building glb_corpus_lst..."
## [1] " Top_n TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 816; Cols: 4"
## TfIdf term freq pos
## condition 207.6392 condition 497 163
## new 125.1790 new 156 484
## used 122.6714 used 237 781
## good 120.6369 good 197 321
## scratches 113.4465 scratches 254 641
## screen 105.8086 screen 210 643
## TfIdf term freq pos
## general 3.854852 general 4 312
## along 3.357156 along 3 44
## models 2.305926 models 2 468
## unsealed 2.190630 unsealed 2 769
## bright 1.981000 bright 2 107
## backwiped 1.137667 backwiped 1 75
## TfIdf term freq pos
## said 1.1376668 said 1 634
## sales 1.1376668 sales 1 636
## seconds 1.1376668 seconds 1 651
## setup 1.1376668 setup 1 661
## shipment 1.1376668 shipment 1 668
## 79in 0.9480557 79in 1 16
## TfIdf term freq pos
## said 1.1376668 said 1 634
## sales 1.1376668 sales 1 636
## seconds 1.1376668 seconds 1 651
## setup 1.1376668 setup 1 661
## shipment 1.1376668 shipment 1 668
## 79in 0.9480557 79in 1 16## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 681; Cols: 4"
## TfIdf term freq pos
## condit 207.4276 condit 492 137
## use 145.7789 use 288 650
## scratch 127.6927 scratch 286 533
## new 125.1790 new 156 404
## good 120.7263 good 197 270
## screen 106.6741 screen 213 534
## TfIdf term freq pos
## 100 34.1933619 100 64 1
## order 17.3376678 order 21 423
## squad 6.6652947 squad 5 588
## space 5.4921507 space 7 581
## rubber 1.6252383 rubber 1 527
## 79in 0.9480557 79in 1 16
## TfIdf term freq pos
## said 1.1376668 said 1 529
## second 1.1376668 second 1 540
## setup 1.1376668 setup 1 548
## shipment 1.1376668 shipment 1 554
## damaged 1.0342426 damaged 1 163
## 79in 0.9480557 79in 1 16
## TfIdf term freq pos
## said 1.1376668 said 1 529
## second 1.1376668 second 1 540
## setup 1.1376668 setup 1 548
## shipment 1.1376668 shipment 1 554
## damaged 1.0342426 damaged 1 163
## 79in 0.9480557 79in 1 16## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.n.post.stop -0.082040646
## TfIdf.sum.post.stop -0.032744659
## terms.n.post.stem -0.081871577
## TfIdf.sum.post.stem -0.034885895
## terms.n.stem.stop.Ratio 0.017153589
## TfIdf.sum.stem.stop.Ratio -0.002724955
## label step_major step_minor bgn end elapsed
## 5 extract.features_build.corpus 4 0 15.749 26.68 10.932
## 6 extract.features_extract.DTM 5 0 26.681 NA NA
## [1] "Extracting TfIDf terms for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 26.681 28.368 1.688
## 7 extract.features_report.DTM 6 0 28.369 NA NA
## [1] "Reporting TfIDf terms for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2659, terms: 681)>>
## Non-/sparse entries: 8345/1802434
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2659, terms: 8)>>
## Non-/sparse entries: 2059/19213
## Sparsity : 90%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df,
## terms_TfIdf_df): object 'full_TfIdf_mtrx' not found## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 28.369 30.499 2.13
## 8 extract.features_bind.DTM 7 0 30.499 NA NA
## [1] "Binding DTM for descr.my..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 30.499 31.2 0.701
## 9 extract.features_bind.DXM 8 0 31.200 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# dsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# dsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# dsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad mini"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini", glb_rsp_var)))## prdline.my productline D.P.mini sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad 2 iPad 2 0 N 139
## 4 iPad mini iPad mini 0 N 138
## 5 iPad mini iPad mini 0 Y 126
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 121
## 8 iPad mini iPad mini 0 <NA> 108
## 9 iPad 1 iPad 1 0 N 102
## 10 iPad Air iPad Air 0 N 102
## 11 iPad Air 2 iPad Air 2 0 N 100
## 12 iPad 4 iPad 4 0 N 93
## 13 Unknown Unknown 0 <NA> 89
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 81
## 16 iPad 3 iPad 3 0 Y 80
## 17 iPad Air iPad Air 0 Y 78
## 18 iPad Air iPad Air 0 <NA> 74
## 19 iPad 3 iPad 3 0 N 73
## 20 iPad Air 2 iPad Air 2 0 Y 71
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad Air 2 iPad Air 2 0 <NA> 62
## 24 iPad mini 3 iPad mini 3 0 N 61
## 25 iPad mini 2 iPad mini 2 0 N 56
## 26 iPad 3 iPad 3 0 <NA> 55
## 27 iPad mini 2 iPad mini 2 0 <NA> 52
## 28 iPad mini 2 iPad mini 2 0 Y 48
## 29 iPad mini 3 iPad mini 3 0 <NA> 35
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad mini iPad mini 1 N 7
## 32 iPad mini iPad mini 1 Y 5
## 33 iPad mini 2 iPad mini 2 1 <NA> 4
## 34 iPad mini Retina iPad mini Retina 0 Y 4
## 35 iPad mini Unknown 1 <NA> 3
## 36 iPad mini iPad mini 1 <NA> 3
## 37 iPad mini 3 iPad mini 3 1 <NA> 3
## 38 iPad mini Retina iPad mini Retina 0 N 3
## 39 iPad mini 2 iPad mini 2 1 N 2
## 40 iPad mini 3 iPad mini 3 1 N 2
## 41 iPad 5 iPad 5 0 Y 1
## 42 iPad mini Unknown 1 N 1
## 43 iPad mini Unknown 1 Y 1
## 44 iPad mini iPad mini 2 Y 1
## 45 iPad mini 2 iPad mini 2 1 Y 1
## 46 iPad mini Retina iPad mini Retina 1 N 1glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad Air"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air", glb_rsp_var)))## prdline.my productline D.P.air sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad mini iPad mini 0 N 145
## 4 iPad 2 iPad 2 0 N 139
## 5 iPad mini iPad mini 0 Y 132
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 120
## 8 iPad mini iPad mini 0 <NA> 111
## 9 iPad 1 iPad 1 0 N 102
## 10 iPad Air iPad Air 0 N 98
## 11 iPad Air 2 iPad Air 2 0 N 97
## 12 iPad 4 iPad 4 0 N 92
## 13 Unknown Unknown 0 <NA> 88
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 80
## 16 iPad 3 iPad 3 0 Y 79
## 17 iPad Air iPad Air 0 Y 75
## 18 iPad 3 iPad 3 0 N 73
## 19 iPad Air iPad Air 0 <NA> 73
## 20 iPad Air 2 iPad Air 2 0 Y 69
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad mini 3 iPad mini 3 0 N 63
## 24 iPad Air 2 iPad Air 2 0 <NA> 60
## 25 iPad mini 2 iPad mini 2 0 N 58
## 26 iPad mini 2 iPad mini 2 0 <NA> 56
## 27 iPad 3 iPad 3 0 <NA> 55
## 28 iPad mini 2 iPad mini 2 0 Y 49
## 29 iPad mini 3 iPad mini 3 0 <NA> 38
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad Air iPad Air 1 N 4
## 32 iPad mini Retina iPad mini Retina 0 N 4
## 33 iPad mini Retina iPad mini Retina 0 Y 4
## 34 iPad Air iPad Air 1 Y 3
## 35 iPad mini Unknown 0 <NA> 3
## 36 iPad Air 2 iPad Air 2 1 <NA> 2
## 37 iPad Air 2 iPad Air 2 1 N 2
## 38 iPad Air 2 iPad Air 2 1 Y 2
## 39 iPad 3 iPad 3 1 Y 1
## 40 iPad 4 iPad 4 1 N 1
## 41 iPad 5 iPad 5 0 Y 1
## 42 iPad Air Unknown 1 <NA> 1
## 43 iPad Air Unknown 1 N 1
## 44 iPad Air Unknown 1 Y 1
## 45 iPad Air iPad Air 1 <NA> 1
## 46 iPad Air 2 iPad Air 2 2 N 1
## 47 iPad mini Unknown 0 N 1
## 48 iPad mini Unknown 0 Y 1glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# dsp_obs(list(prdline.my="Unknown"), all=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
names(tmp_allobs_df) <- "old.prdline.my"
glb_allobs_df$prdline.my <-
plyr::revalue(glb_allobs_df$prdline.my, c(
# "iPad 1" = "iPad",
# "iPad 2" = "iPad2+",
"iPad 3" = "iPad 3+",
"iPad 4" = "iPad 3+",
"iPad 5" = "iPad 3+",
"iPad Air" = "iPadAir",
"iPad Air 2" = "iPadAir",
"iPad mini" = "iPadmini",
"iPad mini 2" = "iPadmini 2+",
"iPad mini 3" = "iPadmini 2+",
"iPad mini Retina" = "iPadmini 2+"
))
tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))## prdline.my old.prdline.my .n
## 1 iPad 2 iPad 2 442
## 2 iPadmini iPad mini 393
## 3 iPad 1 iPad 1 316
## 4 Unknown Unknown 285
## 5 iPadAir iPad Air 257
## 6 iPadAir iPad Air 2 233
## 7 iPad 3+ iPad 4 225
## 8 iPad 3+ iPad 3 208
## 9 iPadmini 2+ iPad mini 2 163
## 10 iPadmini 2+ iPad mini 3 128
## 11 iPadmini 2+ iPad mini Retina 8
## 12 iPad 3+ iPad 5 1print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))## prdline.my .n
## 1 iPadAir 490
## 2 iPad 2 442
## 3 iPad 3+ 434
## 4 iPadmini 393
## 5 iPad 1 316
## 6 iPadmini 2+ 299
## 7 Unknown 285glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 31.200 63.478 32.279
## 10 extract.features_end 9 0 63.479 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 6 extract.features_extract.DTM 5
## 3 extract.features_process.text 3
## 8 extract.features_bind.DTM 7
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 31.200 63.478 32.279 32.278
## 5 0 15.749 26.680 10.932 10.931
## 7 0 28.369 30.499 2.130 2.130
## 6 0 26.681 28.368 1.688 1.687
## 3 0 14.077 15.744 1.667 1.667
## 8 0 30.499 31.200 0.701 0.701
## 2 0 13.816 14.077 0.261 0.261
## 1 0 13.800 13.815 0.015 0.015
## 4 1 15.744 15.749 0.005 0.005
## [1] "Total Elapsed Time: 63.478 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 13.792 64.848 51.056
## 6 cluster.data 4 0 64.848 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 64.848 65.794 0.946
## 7 manage.missing.data 4 1 65.794 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1446 1001 31
## cellular.fctr D.terms.n.post.stop D.TfIdf.sum.post.stop
## 1599 1522 1522
## D.terms.n.post.stem D.TfIdf.sum.post.stem D.T.condit
## 1522 1522 2167
## D.T.use D.T.scratch D.T.new
## 2371 2373 2503
## D.T.good D.T.screen D.T.ipad
## 2462 2446 2427
## D.T.great D.T.work D.T.excel
## 2534 2464 2560
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1521 1522 1522
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log
## 1522 1521 1523
## D.ndgts.log D.npnct01.log D.npnct02.log
## 2428 2581 2659
## D.npnct03.log D.npnct04.log D.npnct05.log
## 2616 2659 2594
## D.npnct06.log D.npnct07.log D.npnct08.log
## 2556 2658 2583
## D.npnct09.log D.npnct10.log D.npnct11.log
## 2643 2650 2303
## D.npnct12.log D.npnct13.log D.npnct14.log
## 2539 1934 2584
## D.npnct15.log D.npnct16.log D.npnct17.log
## 2638 2548 2659
## D.npnct18.log D.npnct19.log D.npnct20.log
## 2658 2659 2659
## D.npnct21.log D.npnct22.log D.npnct23.log
## 2659 2659 2659
## D.npnct24.log D.npnct25.log D.npnct26.log
## 1521 2659 2659
## D.npnct27.log D.npnct28.log D.npnct29.log
## 2659 2651 2659
## D.npnct30.log D.nstopwrds.log D.P.http
## 2659 1664 2659
## D.P.mini D.P.air
## 2625 2639
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline prdline.my descr.my
## 0 0 1521# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1446 1001 31
## cellular.fctr D.terms.n.post.stop D.TfIdf.sum.post.stop
## 1599 1522 1522
## D.terms.n.post.stem D.TfIdf.sum.post.stem D.T.condit
## 1522 1522 2167
## D.T.use D.T.scratch D.T.new
## 2371 2373 2503
## D.T.good D.T.screen D.T.ipad
## 2462 2446 2427
## D.T.great D.T.work D.T.excel
## 2534 2464 2560
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1521 1522 1522
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log
## 1522 1521 1523
## D.ndgts.log D.npnct01.log D.npnct02.log
## 2428 2581 2659
## D.npnct03.log D.npnct04.log D.npnct05.log
## 2616 2659 2594
## D.npnct06.log D.npnct07.log D.npnct08.log
## 2556 2658 2583
## D.npnct09.log D.npnct10.log D.npnct11.log
## 2643 2650 2303
## D.npnct12.log D.npnct13.log D.npnct14.log
## 2539 1934 2584
## D.npnct15.log D.npnct16.log D.npnct17.log
## 2638 2548 2659
## D.npnct18.log D.npnct19.log D.npnct20.log
## 2658 2659 2659
## D.npnct21.log D.npnct22.log D.npnct23.log
## 2659 2659 2659
## D.npnct24.log D.npnct25.log D.npnct26.log
## 1521 2659 2659
## D.npnct27.log D.npnct28.log D.npnct29.log
## 2659 2651 2659
## D.npnct30.log D.nstopwrds.log D.P.http
## 2659 1664 2659
## D.P.mini D.P.air
## 2625 2639
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline prdline.my descr.my
## 0 0 1521Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}
# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 65.794 65.934 0.141
## 8 select.features 5 0 65.935 NA NAStep 5.0: select features
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## sold sold 1.0000000000
## biddable biddable 0.5485860292
## startprice.log startprice.log -0.4668597069
## startprice startprice -0.4556701005
## UniqueID UniqueID -0.1904241613
## idseq.my idseq.my -0.1904241613
## condition.fctr condition.fctr -0.1531007887
## D.npnct05.log D.npnct05.log -0.1178427734
## D.terms.n.post.stop D.terms.n.post.stop -0.0820406460
## D.terms.n.post.stem D.terms.n.post.stem -0.0818715766
## D.npnct14.log D.npnct14.log -0.0784119354
## cellular.fctr cellular.fctr -0.0722383245
## carrier.fctr carrier.fctr -0.0677807650
## D.nwrds.unq.log D.nwrds.unq.log -0.0647054109
## D.ndgts.log D.ndgts.log -0.0633901377
## D.npnct09.log D.npnct09.log -0.0617301891
## D.nwrds.log D.nwrds.log -0.0591808768
## D.nchrs.log D.nchrs.log -0.0566606213
## D.npnct12.log D.npnct12.log -0.0565901515
## D.nuppr.log D.nuppr.log -0.0555211157
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 0.0550700654
## D.npnct28.log D.npnct28.log -0.0523776347
## D.npnct06.log D.npnct06.log -0.0497583609
## D.nstopwrds.log D.nstopwrds.log -0.0472166865
## D.npnct24.log D.npnct24.log -0.0459503420
## D.npnct16.log D.npnct16.log -0.0447153098
## color.fctr color.fctr -0.0426291775
## prdline.my.fctr prdline.my.fctr -0.0403911061
## D.npnct15.log D.npnct15.log 0.0400921803
## D.npnct08.log D.npnct08.log -0.0394718187
## D.T.new D.T.new -0.0381164552
## D.T.condit D.T.condit -0.0378848641
## D.npnct13.log D.npnct13.log -0.0367501751
## D.TfIdf.sum.post.stem D.TfIdf.sum.post.stem -0.0348858951
## D.sum.TfIdf D.sum.TfIdf -0.0348858951
## D.TfIdf.sum.post.stop D.TfIdf.sum.post.stop -0.0327446592
## D.T.excel D.T.excel 0.0267983603
## D.npnct03.log D.npnct03.log 0.0258721719
## D.npnct07.log D.npnct07.log 0.0250156240
## D.T.screen D.T.screen 0.0241354666
## D.npnct10.log D.npnct10.log -0.0240301079
## .rnorm .rnorm -0.0228561937
## D.npnct18.log D.npnct18.log -0.0214919447
## D.npnct11.log D.npnct11.log -0.0188179512
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 0.0171535890
## D.T.ipad D.T.ipad -0.0171141093
## D.T.work D.T.work -0.0132365704
## D.T.use D.T.use 0.0120945268
## D.P.mini D.P.mini -0.0111321924
## storage.fctr storage.fctr -0.0103459049
## D.P.air D.P.air -0.0091681483
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds 0.0082864649
## D.T.great D.T.great 0.0078979293
## D.T.scratch D.T.scratch -0.0073123675
## D.npnct01.log D.npnct01.log 0.0042941114
## D.TfIdf.sum.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio -0.0027249551
## D.T.good D.T.good -0.0004140426
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## sold 1 1.0000000000
## biddable 0 0.5485860292
## startprice.log 0 0.4668597069
## startprice 1 0.4556701005
## UniqueID 1 0.1904241613
## idseq.my 0 0.1904241613
## condition.fctr 0 0.1531007887
## D.npnct05.log 0 0.1178427734
## D.terms.n.post.stop 0 0.0820406460
## D.terms.n.post.stem 0 0.0818715766
## D.npnct14.log 0 0.0784119354
## cellular.fctr 0 0.0722383245
## carrier.fctr 0 0.0677807650
## D.nwrds.unq.log 0 0.0647054109
## D.ndgts.log 0 0.0633901377
## D.npnct09.log 0 0.0617301891
## D.nwrds.log 0 0.0591808768
## D.nchrs.log 0 0.0566606213
## D.npnct12.log 0 0.0565901515
## D.nuppr.log 0 0.0555211157
## D.ratio.nstopwrds.nwrds 0 0.0550700654
## D.npnct28.log 0 0.0523776347
## D.npnct06.log 0 0.0497583609
## D.nstopwrds.log 0 0.0472166865
## D.npnct24.log 0 0.0459503420
## D.npnct16.log 0 0.0447153098
## color.fctr 0 0.0426291775
## prdline.my.fctr 0 0.0403911061
## D.npnct15.log 0 0.0400921803
## D.npnct08.log 0 0.0394718187
## D.T.new 0 0.0381164552
## D.T.condit 0 0.0378848641
## D.npnct13.log 0 0.0367501751
## D.TfIdf.sum.post.stem 0 0.0348858951
## D.sum.TfIdf 0 0.0348858951
## D.TfIdf.sum.post.stop 0 0.0327446592
## D.T.excel 0 0.0267983603
## D.npnct03.log 0 0.0258721719
## D.npnct07.log 0 0.0250156240
## D.T.screen 0 0.0241354666
## D.npnct10.log 0 0.0240301079
## .rnorm 0 0.0228561937
## D.npnct18.log 0 0.0214919447
## D.npnct11.log 0 0.0188179512
## D.terms.n.stem.stop.Ratio 0 0.0171535890
## D.T.ipad 0 0.0171141093
## D.T.work 0 0.0132365704
## D.T.use 0 0.0120945268
## D.P.mini 0 0.0111321924
## storage.fctr 0 0.0103459049
## D.P.air 0 0.0091681483
## D.ratio.sum.TfIdf.nwrds 0 0.0082864649
## D.T.great 0 0.0078979293
## D.T.scratch 0 0.0073123675
## D.npnct01.log 0 0.0042941114
## D.TfIdf.sum.stem.stop.Ratio 0 0.0027249551
## D.T.good 0 0.0004140426
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct17.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 0 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.TfIdf.sum.post.stem, D.sum.TfIdf)=1.0000"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0349"
## [1] "cor(sold.fctr, D.sum.TfIdf)=-0.0349"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nchrs.log, D.nuppr.log)=0.9995"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0567"
## [1] "cor(sold.fctr, D.nuppr.log)=-0.0555"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nuppr.log as highly correlated with
## D.nchrs.log## [1] "cor(D.terms.n.post.stem, D.terms.n.post.stop)=0.9991"
## [1] "cor(sold.fctr, D.terms.n.post.stem)=-0.0819"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0820"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.TfIdf.sum.post.stem, D.TfIdf.sum.post.stop)=0.9974"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0349"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stop)=-0.0327"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stop as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nchrs.log, D.nwrds.unq.log)=0.9935"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0567"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0647"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.nwrds.log, D.nwrds.unq.log)=0.9929"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0592"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0647"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stop)=0.9756"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0647"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0820"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.unq.log as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9654"
## [1] "cor(sold.fctr, D.npnct24.log)=-0.0460"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0551"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9556"
## [1] "cor(sold.fctr, D.npnct06.log)=-0.0498"
## [1] "cor(sold.fctr, D.npnct16.log)=-0.0447"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.TfIdf.sum.post.stem, D.ratio.nstopwrds.nwrds)=-0.9270"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0349"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0551"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stem as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.nstopwrds.log, D.terms.n.post.stop)=0.8963"
## [1] "cor(sold.fctr, D.nstopwrds.log)=-0.0472"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0820"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.ratio.nstopwrds.nwrds, D.terms.n.post.stop)=-0.8687"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0551"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0820"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.terms.n.post.stop## [1] "cor(carrier.fctr, cellular.fctr)=0.8462"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0678"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0722"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## [1] "cor(D.npnct13.log, D.terms.n.post.stop)=0.7383"
## [1] "cor(sold.fctr, D.npnct13.log)=-0.0368"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0820"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct13.log as highly correlated with
## D.terms.n.post.stop## id cor.y exclude.as.feat cor.y.abs
## 68 sold 1.0000000000 1 1.0000000000
## 61 biddable 0.5485860292 0 0.5485860292
## 54 D.ratio.nstopwrds.nwrds 0.0550700654 0 0.0550700654
## 34 D.npnct15.log 0.0400921803 0 0.0400921803
## 6 D.T.excel 0.0267983603 0 0.0267983603
## 22 D.npnct03.log 0.0258721719 0 0.0258721719
## 26 D.npnct07.log 0.0250156240 0 0.0250156240
## 12 D.T.screen 0.0241354666 0 0.0241354666
## 59 D.terms.n.stem.stop.Ratio 0.0171535890 0 0.0171535890
## 13 D.T.use 0.0120945268 0 0.0120945268
## 55 D.ratio.sum.TfIdf.nwrds 0.0082864649 0 0.0082864649
## 8 D.T.great 0.0078979293 0 0.0078979293
## 20 D.npnct01.log 0.0042941114 0 0.0042941114
## 7 D.T.good -0.0004140426 0 0.0004140426
## 17 D.TfIdf.sum.stem.stop.Ratio -0.0027249551 0 0.0027249551
## 11 D.T.scratch -0.0073123675 0 0.0073123675
## 2 D.P.air -0.0091681483 0 0.0091681483
## 71 storage.fctr -0.0103459049 0 0.0103459049
## 4 D.P.mini -0.0111321924 0 0.0111321924
## 14 D.T.work -0.0132365704 0 0.0132365704
## 9 D.T.ipad -0.0171141093 0 0.0171141093
## 30 D.npnct11.log -0.0188179512 0 0.0188179512
## 37 D.npnct18.log -0.0214919447 0 0.0214919447
## 1 .rnorm -0.0228561937 0 0.0228561937
## 29 D.npnct10.log -0.0240301079 0 0.0240301079
## 16 D.TfIdf.sum.post.stop -0.0327446592 0 0.0327446592
## 15 D.TfIdf.sum.post.stem -0.0348858951 0 0.0348858951
## 56 D.sum.TfIdf -0.0348858951 0 0.0348858951
## 32 D.npnct13.log -0.0367501751 0 0.0367501751
## 5 D.T.condit -0.0378848641 0 0.0378848641
## 10 D.T.new -0.0381164552 0 0.0381164552
## 27 D.npnct08.log -0.0394718187 0 0.0394718187
## 67 prdline.my.fctr -0.0403911061 0 0.0403911061
## 64 color.fctr -0.0426291775 0 0.0426291775
## 35 D.npnct16.log -0.0447153098 0 0.0447153098
## 43 D.npnct24.log -0.0459503420 0 0.0459503420
## 50 D.nstopwrds.log -0.0472166865 0 0.0472166865
## 25 D.npnct06.log -0.0497583609 0 0.0497583609
## 47 D.npnct28.log -0.0523776347 0 0.0523776347
## 51 D.nuppr.log -0.0555211157 0 0.0555211157
## 31 D.npnct12.log -0.0565901515 0 0.0565901515
## 18 D.nchrs.log -0.0566606213 0 0.0566606213
## 52 D.nwrds.log -0.0591808768 0 0.0591808768
## 28 D.npnct09.log -0.0617301891 0 0.0617301891
## 19 D.ndgts.log -0.0633901377 0 0.0633901377
## 53 D.nwrds.unq.log -0.0647054109 0 0.0647054109
## 62 carrier.fctr -0.0677807650 0 0.0677807650
## 63 cellular.fctr -0.0722383245 0 0.0722383245
## 33 D.npnct14.log -0.0784119354 0 0.0784119354
## 57 D.terms.n.post.stem -0.0818715766 0 0.0818715766
## 58 D.terms.n.post.stop -0.0820406460 0 0.0820406460
## 24 D.npnct05.log -0.1178427734 0 0.1178427734
## 65 condition.fctr -0.1531007887 0 0.1531007887
## 60 UniqueID -0.1904241613 1 0.1904241613
## 66 idseq.my -0.1904241613 0 0.1904241613
## 69 startprice -0.4556701005 1 0.4556701005
## 70 startprice.log -0.4668597069 0 0.4668597069
## 3 D.P.http NA 0 NA
## 21 D.npnct02.log NA 0 NA
## 23 D.npnct04.log NA 0 NA
## 36 D.npnct17.log NA 0 NA
## 38 D.npnct19.log NA 0 NA
## 39 D.npnct20.log NA 0 NA
## 40 D.npnct21.log NA 0 NA
## 41 D.npnct22.log NA 0 NA
## 42 D.npnct23.log NA 0 NA
## 44 D.npnct25.log NA 0 NA
## 45 D.npnct26.log NA 0 NA
## 46 D.npnct27.log NA 0 NA
## 48 D.npnct29.log NA 0 NA
## 49 D.npnct30.log NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## 68 <NA> 1.163953 0.10746910 FALSE FALSE
## 61 <NA> 1.223417 0.10746910 FALSE FALSE
## 54 D.terms.n.post.stop 12.903614 4.03009135 FALSE FALSE
## 34 <NA> 153.500000 0.16120365 FALSE TRUE
## 6 <NA> 149.833333 0.75228372 FALSE TRUE
## 22 <NA> 83.318182 0.16120365 FALSE TRUE
## 26 <NA> 1860.000000 0.10746910 FALSE TRUE
## 12 <NA> 56.966667 0.80601827 FALSE TRUE
## 59 <NA> 77.913043 0.48361096 FALSE TRUE
## 13 <NA> 51.750000 0.91348737 FALSE TRUE
## 55 <NA> 63.058824 34.87372380 FALSE FALSE
## 8 <NA> 98.777778 0.80601827 FALSE TRUE
## 20 <NA> 53.029412 0.32240731 FALSE TRUE
## 7 <NA> 49.257143 0.85975282 FALSE TRUE
## 17 <NA> 65.235294 32.93927996 FALSE FALSE
## 11 <NA> 43.631579 0.85975282 FALSE TRUE
## 2 <NA> 123.000000 0.16120365 FALSE TRUE
## 71 <NA> 2.739003 0.26867276 FALSE FALSE
## 4 <NA> 92.000000 0.16120365 FALSE TRUE
## 14 <NA> 68.880000 0.69854917 FALSE TRUE
## 9 <NA> 44.631579 0.85975282 FALSE TRUE
## 30 <NA> 9.385965 0.37614186 FALSE FALSE
## 37 <NA> 1860.000000 0.10746910 FALSE TRUE
## 1 <NA> 1.000000 100.00000000 FALSE FALSE
## 29 <NA> 309.000000 0.16120365 FALSE TRUE
## 16 D.TfIdf.sum.post.stem 63.058824 34.39011284 FALSE FALSE
## 15 D.ratio.nstopwrds.nwrds 63.058824 34.33637829 FALSE FALSE
## 56 D.TfIdf.sum.post.stem 63.058824 34.33637829 FALSE FALSE
## 32 D.terms.n.post.stop 5.210728 0.32240731 FALSE FALSE
## 5 <NA> 25.779661 0.91348737 FALSE TRUE
## 10 <NA> 92.263158 0.85975282 FALSE TRUE
## 27 <NA> 69.653846 0.21493821 FALSE TRUE
## 67 <NA> 1.135048 0.37614186 FALSE FALSE
## 64 <NA> 1.576837 0.26867276 FALSE FALSE
## 35 D.npnct06.log 31.280702 0.16120365 FALSE TRUE
## 43 D.ratio.nstopwrds.nwrds 1.355696 0.10746910 FALSE FALSE
## 50 D.terms.n.post.stop 13.764706 0.80601827 FALSE FALSE
## 25 <NA> 33.773585 0.16120365 FALSE TRUE
## 47 <NA> 463.750000 0.16120365 FALSE TRUE
## 51 D.nchrs.log 18.500000 4.40623321 FALSE FALSE
## 31 <NA> 26.848485 0.21493821 FALSE TRUE
## 18 D.nwrds.unq.log 15.750000 5.64212789 FALSE FALSE
## 52 D.nwrds.unq.log 12.903614 1.28962923 FALSE FALSE
## 28 <NA> 308.666667 0.21493821 FALSE TRUE
## 19 <NA> 27.063492 0.69854917 FALSE TRUE
## 53 D.terms.n.post.stop 8.246154 0.80601827 FALSE FALSE
## 62 cellular.fctr 3.192529 0.37614186 FALSE FALSE
## 63 <NA> 2.120229 0.16120365 FALSE FALSE
## 33 <NA> 35.372549 0.26867276 FALSE TRUE
## 57 D.terms.n.post.stop 8.246154 0.80601827 FALSE FALSE
## 58 <NA> 8.859504 0.80601827 FALSE FALSE
## 24 <NA> 40.355556 0.10746910 FALSE TRUE
## 65 <NA> 4.006920 0.32240731 FALSE FALSE
## 60 <NA> 1.000000 100.00000000 FALSE FALSE
## 66 <NA> 1.000000 100.00000000 FALSE FALSE
## 69 <NA> 2.807692 30.14508329 FALSE FALSE
## 70 <NA> 2.807692 30.14508329 FALSE FALSE
## 3 <NA> 0.000000 0.05373455 TRUE TRUE
## 21 <NA> 0.000000 0.05373455 TRUE TRUE
## 23 <NA> 0.000000 0.05373455 TRUE TRUE
## 36 <NA> 0.000000 0.05373455 TRUE TRUE
## 38 <NA> 0.000000 0.05373455 TRUE TRUE
## 39 <NA> 0.000000 0.05373455 TRUE TRUE
## 40 <NA> 0.000000 0.05373455 TRUE TRUE
## 41 <NA> 0.000000 0.05373455 TRUE TRUE
## 42 <NA> 0.000000 0.05373455 TRUE TRUE
## 44 <NA> 0.000000 0.05373455 TRUE TRUE
## 45 <NA> 0.000000 0.05373455 TRUE TRUE
## 46 <NA> 0.000000 0.05373455 TRUE TRUE
## 48 <NA> 0.000000 0.05373455 TRUE TRUE
## 49 <NA> 0.000000 0.05373455 TRUE TRUE
## myNearZV is.cor.y.abs.low
## 68 FALSE FALSE
## 61 FALSE FALSE
## 54 FALSE FALSE
## 34 FALSE FALSE
## 6 FALSE FALSE
## 22 FALSE FALSE
## 26 TRUE FALSE
## 12 FALSE FALSE
## 59 FALSE TRUE
## 13 FALSE TRUE
## 55 FALSE TRUE
## 8 FALSE TRUE
## 20 FALSE TRUE
## 7 FALSE TRUE
## 17 FALSE TRUE
## 11 FALSE TRUE
## 2 FALSE TRUE
## 71 FALSE TRUE
## 4 FALSE TRUE
## 14 FALSE TRUE
## 9 FALSE TRUE
## 30 FALSE TRUE
## 37 TRUE TRUE
## 1 FALSE FALSE
## 29 FALSE FALSE
## 16 FALSE FALSE
## 15 FALSE FALSE
## 56 FALSE FALSE
## 32 FALSE FALSE
## 5 FALSE FALSE
## 10 FALSE FALSE
## 27 FALSE FALSE
## 67 FALSE FALSE
## 64 FALSE FALSE
## 35 FALSE FALSE
## 43 FALSE FALSE
## 50 FALSE FALSE
## 25 FALSE FALSE
## 47 FALSE FALSE
## 51 FALSE FALSE
## 31 FALSE FALSE
## 18 FALSE FALSE
## 52 FALSE FALSE
## 28 FALSE FALSE
## 19 FALSE FALSE
## 53 FALSE FALSE
## 62 FALSE FALSE
## 63 FALSE FALSE
## 33 FALSE FALSE
## 57 FALSE FALSE
## 58 FALSE FALSE
## 24 FALSE FALSE
## 65 FALSE FALSE
## 60 FALSE FALSE
## 66 FALSE FALSE
## 69 FALSE FALSE
## 70 FALSE FALSE
## 3 TRUE NA
## 21 TRUE NA
## 23 TRUE NA
## 36 TRUE NA
## 38 TRUE NA
## 39 TRUE NA
## 40 TRUE NA
## 41 TRUE NA
## 42 TRUE NA
## 44 TRUE NA
## 45 TRUE NA
## 46 TRUE NA
## 48 TRUE NA
## 49 TRUE NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 10 rows containing missing values (geom_point).## Warning: Removed 10 rows containing missing values (geom_point).## Warning: Removed 10 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 26 D.npnct07.log 0.02501562 0 0.02501562 <NA>
## 37 D.npnct18.log -0.02149194 0 0.02149194 <NA>
## 3 D.P.http NA 0 NA <NA>
## 21 D.npnct02.log NA 0 NA <NA>
## 23 D.npnct04.log NA 0 NA <NA>
## 36 D.npnct17.log NA 0 NA <NA>
## 38 D.npnct19.log NA 0 NA <NA>
## 39 D.npnct20.log NA 0 NA <NA>
## 40 D.npnct21.log NA 0 NA <NA>
## 41 D.npnct22.log NA 0 NA <NA>
## 42 D.npnct23.log NA 0 NA <NA>
## 44 D.npnct25.log NA 0 NA <NA>
## 45 D.npnct26.log NA 0 NA <NA>
## 46 D.npnct27.log NA 0 NA <NA>
## 48 D.npnct29.log NA 0 NA <NA>
## 49 D.npnct30.log NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 26 1860 0.10746910 FALSE TRUE TRUE FALSE
## 37 1860 0.10746910 FALSE TRUE TRUE TRUE
## 3 0 0.05373455 TRUE TRUE TRUE NA
## 21 0 0.05373455 TRUE TRUE TRUE NA
## 23 0 0.05373455 TRUE TRUE TRUE NA
## 36 0 0.05373455 TRUE TRUE TRUE NA
## 38 0 0.05373455 TRUE TRUE TRUE NA
## 39 0 0.05373455 TRUE TRUE TRUE NA
## 40 0 0.05373455 TRUE TRUE TRUE NA
## 41 0 0.05373455 TRUE TRUE TRUE NA
## 42 0 0.05373455 TRUE TRUE TRUE NA
## 44 0 0.05373455 TRUE TRUE TRUE NA
## 45 0 0.05373455 TRUE TRUE TRUE NA
## 46 0 0.05373455 TRUE TRUE TRUE NA
## 48 0 0.05373455 TRUE TRUE TRUE NA
## 49 0 0.05373455 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1446 1001 31
## cellular.fctr D.terms.n.post.stop D.TfIdf.sum.post.stop
## 1599 1522 1522
## D.terms.n.post.stem D.TfIdf.sum.post.stem D.T.condit
## 1522 1522 2167
## D.T.use D.T.scratch D.T.new
## 2371 2373 2503
## D.T.good D.T.screen D.T.ipad
## 2462 2446 2427
## D.T.great D.T.work D.T.excel
## 2534 2464 2560
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1521 1522 1522
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log
## 1522 1521 1523
## D.ndgts.log D.npnct01.log D.npnct03.log
## 2428 2581 2616
## D.npnct05.log D.npnct06.log D.npnct08.log
## 2594 2556 2583
## D.npnct09.log D.npnct10.log D.npnct11.log
## 2643 2650 2303
## D.npnct12.log D.npnct13.log D.npnct14.log
## 2539 1934 2584
## D.npnct15.log D.npnct16.log D.npnct24.log
## 2638 2548 1521
## D.npnct28.log D.nstopwrds.log D.P.mini
## 2651 1664 2625
## D.P.air
## 2639
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline prdline.my descr.my
## 0 0 1521# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 65.935 67.44 1.505
## 9 partition.data.training 6 0 67.441 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_vars)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_vars)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_vars),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_vars))
trnobs_freq_df <- orderBy(reformulate(glb_category_vars),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_vars))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_vars,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_vars=allobs_freq_df[, glb_category_vars])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_vars)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_vars),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## clusterif (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_vars)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_vars)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_vars
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## sold.0 sold.1 sold.NA
## NA NA 798
## Fit 525 451 NA
## OOB 476 409 NA
## sold.0 sold.1 sold.NA
## NA NA 1
## Fit 0.5379098 0.4620902 NA
## OOB 0.5378531 0.4621469 NA
## prdline.my .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 154 171 0.1929825 0.1932203
## 5 iPadAir 137 152 0.1716792 0.1717514
## 4 iPad 3+ 123 136 0.1541353 0.1536723
## 6 iPadmini 114 126 0.1428571 0.1423729
## 7 iPadmini 2+ 94 104 0.1177945 0.1175141
## 2 iPad 1 89 99 0.1115288 0.1118644
## 1 Unknown 87 97 0.1090226 0.1096045# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 71 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## sold.fctr sold.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 68 sold 1.0000000 TRUE 1.0000000 <NA>
## 60 UniqueID -0.1904242 TRUE 0.1904242 <NA>
## sold.fctr sold.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 68 1.163953 0.1074691 FALSE FALSE FALSE FALSE
## 60 1.000000 100.0000000 FALSE FALSE FALSE FALSE
## sold.fctr NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 68 NA TRUE NA NA
## 60 NA FALSE TRUE NA
## sold.fctr NA NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct07.log" "D.npnct18.log" "D.P.http" "D.npnct02.log"
## [5] "D.npnct04.log" "D.npnct17.log" "D.npnct19.log" "D.npnct20.log"
## [9] "D.npnct21.log" "D.npnct22.log" "D.npnct23.log" "D.npnct25.log"
## [13] "D.npnct26.log" "D.npnct27.log" "D.npnct29.log" "D.npnct30.log"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2659 67print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 1861 66print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 976 66print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 885 66print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 798 66# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 67.441 68.28 0.839
## 10 fit.models 7 0 68.280 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.5379098 0.4620902
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379098 0.4620902
## 2 0.5379098 0.4620902
## 3 0.5379098 0.4620902
## 4 0.5379098 0.4620902
## 5 0.5379098 0.4620902
## 6 0.5379098 0.4620902
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 525
## 2 Y 451
## Prediction
## Reference N Y
## N 525 0
## Y 451 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379098e-01 0.000000e+00 5.060449e-01 5.695455e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 5.131267e-01 1.189217e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379098 0.4620902
## 2 0.5379098 0.4620902
## 3 0.5379098 0.4620902
## 4 0.5379098 0.4620902
## 5 0.5379098 0.4620902
## 6 0.5379098 0.4620902
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 476
## 2 Y 409
## Prediction
## Reference N Y
## N 476 0
## Y 409 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.378531e-01 0.000000e+00 5.043611e-01 5.710923e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 5.137840e-01 1.646732e-90
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.323 0.003 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.5379098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060449 0.5695455 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5378531
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5043611 0.5710923 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6320953
## 3 0.2 0.6320953
## 4 0.3 0.6320953
## 5 0.4 0.6320953
## 6 0.5 0.4824561
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 525
## 2 Y 451
## Prediction
## Reference N Y
## N 0 525
## Y 0 451
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.620902e-01 0.000000e+00 4.304545e-01 4.939551e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 9.999991e-01 9.406788e-116
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6321484
## 3 0.2 0.6321484
## 4 0.3 0.6321484
## 5 0.4 0.6321484
## 6 0.5 0.4847001
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 476
## 2 Y 409
## Prediction
## Reference N Y
## N 0 476
## Y 0 409
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.621469e-01 0.000000e+00 4.289077e-01 4.956389e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 9.999972e-01 4.314099e-105
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.259 0.002 0.5143786
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.6320953 0.4620902
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.4304545 0.4939551 0 0.5214656
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6321484 0.4621469
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4289077 0.4956389 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, startprice.log"## Loading required package: rpart## Fitting cp = 0.488 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 976
##
## CP nsplit rel error
## 1 0.4878049 0 1
##
## Node number 1: 976 observations
## predicted class=N expected loss=0.4620902 P(node) =1
## class counts: 525 451
## probabilities: 0.538 0.462
##
## n= 976
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 976 451 N (0.5379098 0.4620902) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 525
## 2 Y 451
## Prediction
## Reference N Y
## N 525 0
## Y 451 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379098e-01 0.000000e+00 5.060449e-01 5.695455e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 5.131267e-01 1.189217e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 476
## 2 Y 409
## Prediction
## Reference N Y
## N 476 0
## Y 409 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.378531e-01 0.000000e+00 5.043611e-01 5.710923e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 5.137840e-01 1.646732e-90
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart biddable, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.624 0.011
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5379098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060449 0.5695455 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5378531
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5043611 0.5710923 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, startprice.log"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 976
##
## CP nsplit rel error
## 1 0.4878048780 0 1.0000000
## 2 0.0166297118 1 0.5121951
## 3 0.0110864745 3 0.4789357
## 4 0.0099778271 4 0.4678492
## 5 0.0066518847 6 0.4478936
## 6 0.0055432373 8 0.4345898
## 7 0.0033259424 10 0.4235033
## 8 0.0011086475 14 0.4101996
## 9 0.0007390983 20 0.4035477
## 10 0.0002217295 23 0.4013304
## 11 0.0000000000 33 0.3991131
##
## Variable importance
## startprice.log biddable
## 52 48
##
## Node number 1: 976 observations, complexity param=0.4878049
## predicted class=N expected loss=0.4620902 P(node) =1
## class counts: 525 451
## probabilities: 0.538 0.462
## left son=2 (536 obs) right son=3 (440 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=132.8253, (0 missing)
## startprice.log < 4.610145 to the right, improve=113.3071, (0 missing)
## Surrogate splits:
## startprice.log < 5.030417 to the right, agree=0.738, adj=0.418, (0 split)
##
## Node number 2: 536 observations, complexity param=0.003325942
## predicted class=N expected loss=0.2257463 P(node) =0.5491803
## class counts: 415 121
## probabilities: 0.774 0.226
## left son=4 (333 obs) right son=5 (203 obs)
## Primary splits:
## startprice.log < 5.344364 to the right, improve=6.453827, (0 missing)
##
## Node number 3: 440 observations, complexity param=0.01662971
## predicted class=Y expected loss=0.25 P(node) =0.4508197
## class counts: 110 330
## probabilities: 0.250 0.750
## left son=6 (163 obs) right son=7 (277 obs)
## Primary splits:
## startprice.log < 4.923459 to the right, improve=38.16294, (0 missing)
##
## Node number 4: 333 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1651652 P(node) =0.3411885
## class counts: 278 55
## probabilities: 0.835 0.165
## left son=8 (123 obs) right son=9 (210 obs)
## Primary splits:
## startprice.log < 5.972491 to the right, improve=1.379799, (0 missing)
##
## Node number 5: 203 observations, complexity param=0.003325942
## predicted class=N expected loss=0.3251232 P(node) =0.2079918
## class counts: 137 66
## probabilities: 0.675 0.325
## left son=10 (137 obs) right son=11 (66 obs)
## Primary splits:
## startprice.log < 4.574498 to the right, improve=1.921612, (0 missing)
##
## Node number 6: 163 observations, complexity param=0.01662971
## predicted class=N expected loss=0.4785276 P(node) =0.1670082
## class counts: 85 78
## probabilities: 0.521 0.479
## left son=12 (21 obs) right son=13 (142 obs)
## Primary splits:
## startprice.log < 6.024113 to the right, improve=5.432188, (0 missing)
##
## Node number 7: 277 observations
## predicted class=Y expected loss=0.09025271 P(node) =0.2838115
## class counts: 25 252
## probabilities: 0.090 0.910
##
## Node number 8: 123 observations
## predicted class=N expected loss=0.1056911 P(node) =0.1260246
## class counts: 110 13
## probabilities: 0.894 0.106
##
## Node number 9: 210 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.2 P(node) =0.2151639
## class counts: 168 42
## probabilities: 0.800 0.200
## left son=18 (200 obs) right son=19 (10 obs)
## Primary splits:
## startprice.log < 5.940158 to the left, improve=1.89, (0 missing)
##
## Node number 10: 137 observations, complexity param=0.001108647
## predicted class=N expected loss=0.2773723 P(node) =0.1403689
## class counts: 99 38
## probabilities: 0.723 0.277
## left son=20 (23 obs) right son=21 (114 obs)
## Primary splits:
## startprice.log < 4.828274 to the left, improve=2.004376, (0 missing)
##
## Node number 11: 66 observations, complexity param=0.003325942
## predicted class=N expected loss=0.4242424 P(node) =0.06762295
## class counts: 38 28
## probabilities: 0.576 0.424
## left son=22 (11 obs) right son=23 (55 obs)
## Primary splits:
## startprice.log < 3.365867 to the left, improve=1.551515, (0 missing)
##
## Node number 12: 21 observations
## predicted class=N expected loss=0.1428571 P(node) =0.02151639
## class counts: 18 3
## probabilities: 0.857 0.143
##
## Node number 13: 142 observations, complexity param=0.01108647
## predicted class=Y expected loss=0.471831 P(node) =0.1454918
## class counts: 67 75
## probabilities: 0.472 0.528
## left son=26 (9 obs) right son=27 (133 obs)
## Primary splits:
## startprice.log < 5.010535 to the left, improve=1.798875, (0 missing)
##
## Node number 18: 200 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.185 P(node) =0.204918
## class counts: 163 37
## probabilities: 0.815 0.185
## left son=36 (7 obs) right son=37 (193 obs)
## Primary splits:
## startprice.log < 5.365942 to the left, improve=0.4965285, (0 missing)
##
## Node number 19: 10 observations
## predicted class=N expected loss=0.5 P(node) =0.0102459
## class counts: 5 5
## probabilities: 0.500 0.500
##
## Node number 20: 23 observations
## predicted class=N expected loss=0.08695652 P(node) =0.02356557
## class counts: 21 2
## probabilities: 0.913 0.087
##
## Node number 21: 114 observations, complexity param=0.001108647
## predicted class=N expected loss=0.3157895 P(node) =0.1168033
## class counts: 78 36
## probabilities: 0.684 0.316
## left son=42 (107 obs) right son=43 (7 obs)
## Primary splits:
## startprice.log < 4.917424 to the right, improve=0.9747734, (0 missing)
##
## Node number 22: 11 observations
## predicted class=N expected loss=0.1818182 P(node) =0.01127049
## class counts: 9 2
## probabilities: 0.818 0.182
##
## Node number 23: 55 observations, complexity param=0.003325942
## predicted class=N expected loss=0.4727273 P(node) =0.05635246
## class counts: 29 26
## probabilities: 0.527 0.473
## left son=46 (35 obs) right son=47 (20 obs)
## Primary splits:
## startprice.log < 4.248424 to the right, improve=1.975325, (0 missing)
##
## Node number 26: 9 observations
## predicted class=N expected loss=0.2222222 P(node) =0.009221311
## class counts: 7 2
## probabilities: 0.778 0.222
##
## Node number 27: 133 observations, complexity param=0.009977827
## predicted class=Y expected loss=0.4511278 P(node) =0.1362705
## class counts: 60 73
## probabilities: 0.451 0.549
## left son=54 (104 obs) right son=55 (29 obs)
## Primary splits:
## startprice.log < 5.176086 to the right, improve=0.8381365, (0 missing)
##
## Node number 36: 7 observations
## predicted class=N expected loss=0 P(node) =0.007172131
## class counts: 7 0
## probabilities: 1.000 0.000
##
## Node number 37: 193 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1917098 P(node) =0.1977459
## class counts: 156 37
## probabilities: 0.808 0.192
## left son=74 (186 obs) right son=75 (7 obs)
## Primary splits:
## startprice.log < 5.393605 to the right, improve=0.8150076, (0 missing)
##
## Node number 42: 107 observations, complexity param=0.001108647
## predicted class=N expected loss=0.2990654 P(node) =0.1096311
## class counts: 75 32
## probabilities: 0.701 0.299
## left son=84 (8 obs) right son=85 (99 obs)
## Primary splits:
## startprice.log < 5.005922 to the left, improve=1.546682, (0 missing)
##
## Node number 43: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.007172131
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 46: 35 observations, complexity param=0.001108647
## predicted class=N expected loss=0.3714286 P(node) =0.03586066
## class counts: 22 13
## probabilities: 0.629 0.371
## left son=92 (10 obs) right son=93 (25 obs)
## Primary splits:
## startprice.log < 4.375675 to the left, improve=0.8228571, (0 missing)
##
## Node number 47: 20 observations
## predicted class=Y expected loss=0.35 P(node) =0.0204918
## class counts: 7 13
## probabilities: 0.350 0.650
##
## Node number 54: 104 observations, complexity param=0.009977827
## predicted class=Y expected loss=0.4807692 P(node) =0.1065574
## class counts: 50 54
## probabilities: 0.481 0.519
## left son=108 (43 obs) right son=109 (61 obs)
## Primary splits:
## startprice.log < 5.501177 to the left, improve=2.250183, (0 missing)
##
## Node number 55: 29 observations
## predicted class=Y expected loss=0.3448276 P(node) =0.02971311
## class counts: 10 19
## probabilities: 0.345 0.655
##
## Node number 74: 186 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1827957 P(node) =0.1905738
## class counts: 152 34
## probabilities: 0.817 0.183
## left son=148 (155 obs) right son=149 (31 obs)
## Primary splits:
## startprice.log < 5.843472 to the left, improve=0.4215054, (0 missing)
##
## Node number 75: 7 observations
## predicted class=N expected loss=0.4285714 P(node) =0.007172131
## class counts: 4 3
## probabilities: 0.571 0.429
##
## Node number 84: 8 observations
## predicted class=N expected loss=0 P(node) =0.008196721
## class counts: 8 0
## probabilities: 1.000 0.000
##
## Node number 85: 99 observations, complexity param=0.001108647
## predicted class=N expected loss=0.3232323 P(node) =0.1014344
## class counts: 67 32
## probabilities: 0.677 0.323
## left son=170 (84 obs) right son=171 (15 obs)
## Primary splits:
## startprice.log < 5.023794 to the right, improve=1.56075, (0 missing)
##
## Node number 92: 10 observations
## predicted class=N expected loss=0.2 P(node) =0.0102459
## class counts: 8 2
## probabilities: 0.800 0.200
##
## Node number 93: 25 observations, complexity param=0.001108647
## predicted class=N expected loss=0.44 P(node) =0.02561475
## class counts: 14 11
## probabilities: 0.560 0.440
## left son=186 (18 obs) right son=187 (7 obs)
## Primary splits:
## startprice.log < 4.430386 to the right, improve=0.335873, (0 missing)
##
## Node number 108: 43 observations, complexity param=0.006651885
## predicted class=N expected loss=0.3953488 P(node) =0.04405738
## class counts: 26 17
## probabilities: 0.605 0.395
## left son=216 (16 obs) right son=217 (27 obs)
## Primary splits:
## startprice.log < 5.322712 to the right, improve=3.724806, (0 missing)
##
## Node number 109: 61 observations, complexity param=0.005543237
## predicted class=Y expected loss=0.3934426 P(node) =0.0625
## class counts: 24 37
## probabilities: 0.393 0.607
## left son=218 (46 obs) right son=219 (15 obs)
## Primary splits:
## startprice.log < 5.531362 to the right, improve=1.488667, (0 missing)
##
## Node number 148: 155 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1677419 P(node) =0.1588115
## class counts: 129 26
## probabilities: 0.832 0.168
## left son=296 (33 obs) right son=297 (122 obs)
## Primary splits:
## startprice.log < 5.768305 to the right, improve=0.495005, (0 missing)
##
## Node number 149: 31 observations
## predicted class=N expected loss=0.2580645 P(node) =0.0317623
## class counts: 23 8
## probabilities: 0.742 0.258
##
## Node number 170: 84 observations, complexity param=0.0007390983
## predicted class=N expected loss=0.2857143 P(node) =0.08606557
## class counts: 60 24
## probabilities: 0.714 0.286
## left son=340 (16 obs) right son=341 (68 obs)
## Primary splits:
## startprice.log < 5.135337 to the left, improve=0.3813025, (0 missing)
##
## Node number 171: 15 observations
## predicted class=Y expected loss=0.4666667 P(node) =0.01536885
## class counts: 7 8
## probabilities: 0.467 0.533
##
## Node number 186: 18 observations
## predicted class=N expected loss=0.3888889 P(node) =0.01844262
## class counts: 11 7
## probabilities: 0.611 0.389
##
## Node number 187: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.007172131
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 216: 16 observations
## predicted class=N expected loss=0.125 P(node) =0.01639344
## class counts: 14 2
## probabilities: 0.875 0.125
##
## Node number 217: 27 observations, complexity param=0.006651885
## predicted class=Y expected loss=0.4444444 P(node) =0.02766393
## class counts: 12 15
## probabilities: 0.444 0.556
## left son=434 (7 obs) right son=435 (20 obs)
## Primary splits:
## startprice.log < 5.220329 to the left, improve=1.37619, (0 missing)
##
## Node number 218: 46 observations, complexity param=0.005543237
## predicted class=Y expected loss=0.4565217 P(node) =0.04713115
## class counts: 21 25
## probabilities: 0.457 0.543
## left son=436 (9 obs) right son=437 (37 obs)
## Primary splits:
## startprice.log < 5.600228 to the left, improve=2.30957, (0 missing)
##
## Node number 219: 15 observations
## predicted class=Y expected loss=0.2 P(node) =0.01536885
## class counts: 3 12
## probabilities: 0.200 0.800
##
## Node number 296: 33 observations
## predicted class=N expected loss=0.09090909 P(node) =0.03381148
## class counts: 30 3
## probabilities: 0.909 0.091
##
## Node number 297: 122 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1885246 P(node) =0.125
## class counts: 99 23
## probabilities: 0.811 0.189
## left son=594 (111 obs) right son=595 (11 obs)
## Primary splits:
## startprice.log < 5.720131 to the left, improve=1.711161, (0 missing)
##
## Node number 340: 16 observations
## predicted class=N expected loss=0.1875 P(node) =0.01639344
## class counts: 13 3
## probabilities: 0.812 0.188
##
## Node number 341: 68 observations, complexity param=0.0007390983
## predicted class=N expected loss=0.3088235 P(node) =0.06967213
## class counts: 47 21
## probabilities: 0.691 0.309
## left son=682 (43 obs) right son=683 (25 obs)
## Primary splits:
## startprice.log < 5.227067 to the right, improve=0.6573187, (0 missing)
##
## Node number 434: 7 observations
## predicted class=N expected loss=0.2857143 P(node) =0.007172131
## class counts: 5 2
## probabilities: 0.714 0.286
##
## Node number 435: 20 observations
## predicted class=Y expected loss=0.35 P(node) =0.0204918
## class counts: 7 13
## probabilities: 0.350 0.650
##
## Node number 436: 9 observations
## predicted class=N expected loss=0.2222222 P(node) =0.009221311
## class counts: 7 2
## probabilities: 0.778 0.222
##
## Node number 437: 37 observations
## predicted class=Y expected loss=0.3783784 P(node) =0.03790984
## class counts: 14 23
## probabilities: 0.378 0.622
##
## Node number 594: 111 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1621622 P(node) =0.1137295
## class counts: 93 18
## probabilities: 0.838 0.162
## left son=1188 (67 obs) right son=1189 (44 obs)
## Primary splits:
## startprice.log < 5.527188 to the right, improve=0.6180645, (0 missing)
##
## Node number 595: 11 observations
## predicted class=N expected loss=0.4545455 P(node) =0.01127049
## class counts: 6 5
## probabilities: 0.545 0.455
##
## Node number 682: 43 observations
## predicted class=N expected loss=0.255814 P(node) =0.04405738
## class counts: 32 11
## probabilities: 0.744 0.256
##
## Node number 683: 25 observations, complexity param=0.0007390983
## predicted class=N expected loss=0.4 P(node) =0.02561475
## class counts: 15 10
## probabilities: 0.600 0.400
## left son=1366 (14 obs) right son=1367 (11 obs)
## Primary splits:
## startprice.log < 5.192818 to the left, improve=0.8311688, (0 missing)
##
## Node number 1188: 67 observations
## predicted class=N expected loss=0.119403 P(node) =0.06864754
## class counts: 59 8
## probabilities: 0.881 0.119
##
## Node number 1189: 44 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.2272727 P(node) =0.04508197
## class counts: 34 10
## probabilities: 0.773 0.227
## left son=2378 (18 obs) right son=2379 (26 obs)
## Primary splits:
## startprice.log < 5.476045 to the left, improve=0.8220668, (0 missing)
##
## Node number 1366: 14 observations
## predicted class=N expected loss=0.2857143 P(node) =0.01434426
## class counts: 10 4
## probabilities: 0.714 0.286
##
## Node number 1367: 11 observations
## predicted class=Y expected loss=0.4545455 P(node) =0.01127049
## class counts: 5 6
## probabilities: 0.455 0.545
##
## Node number 2378: 18 observations
## predicted class=N expected loss=0.1111111 P(node) =0.01844262
## class counts: 16 2
## probabilities: 0.889 0.111
##
## Node number 2379: 26 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.3076923 P(node) =0.02663934
## class counts: 18 8
## probabilities: 0.692 0.308
## left son=4758 (19 obs) right son=4759 (7 obs)
## Primary splits:
## startprice.log < 5.507343 to the right, improve=1.332562, (0 missing)
##
## Node number 4758: 19 observations
## predicted class=N expected loss=0.2105263 P(node) =0.01946721
## class counts: 15 4
## probabilities: 0.789 0.211
##
## Node number 4759: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.007172131
## class counts: 3 4
## probabilities: 0.429 0.571
##
## n= 976
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 976 451 N (0.53790984 0.46209016)
## 2) biddable< 0.5 536 121 N (0.77425373 0.22574627)
## 4) startprice.log>=5.344364 333 55 N (0.83483483 0.16516517)
## 8) startprice.log>=5.972491 123 13 N (0.89430894 0.10569106) *
## 9) startprice.log< 5.972491 210 42 N (0.80000000 0.20000000)
## 18) startprice.log< 5.940158 200 37 N (0.81500000 0.18500000)
## 36) startprice.log< 5.365942 7 0 N (1.00000000 0.00000000) *
## 37) startprice.log>=5.365942 193 37 N (0.80829016 0.19170984)
## 74) startprice.log>=5.393605 186 34 N (0.81720430 0.18279570)
## 148) startprice.log< 5.843472 155 26 N (0.83225806 0.16774194)
## 296) startprice.log>=5.768305 33 3 N (0.90909091 0.09090909) *
## 297) startprice.log< 5.768305 122 23 N (0.81147541 0.18852459)
## 594) startprice.log< 5.720131 111 18 N (0.83783784 0.16216216)
## 1188) startprice.log>=5.527188 67 8 N (0.88059701 0.11940299) *
## 1189) startprice.log< 5.527188 44 10 N (0.77272727 0.22727273)
## 2378) startprice.log< 5.476045 18 2 N (0.88888889 0.11111111) *
## 2379) startprice.log>=5.476045 26 8 N (0.69230769 0.30769231)
## 4758) startprice.log>=5.507343 19 4 N (0.78947368 0.21052632) *
## 4759) startprice.log< 5.507343 7 3 Y (0.42857143 0.57142857) *
## 595) startprice.log>=5.720131 11 5 N (0.54545455 0.45454545) *
## 149) startprice.log>=5.843472 31 8 N (0.74193548 0.25806452) *
## 75) startprice.log< 5.393605 7 3 N (0.57142857 0.42857143) *
## 19) startprice.log>=5.940158 10 5 N (0.50000000 0.50000000) *
## 5) startprice.log< 5.344364 203 66 N (0.67487685 0.32512315)
## 10) startprice.log>=4.574498 137 38 N (0.72262774 0.27737226)
## 20) startprice.log< 4.828274 23 2 N (0.91304348 0.08695652) *
## 21) startprice.log>=4.828274 114 36 N (0.68421053 0.31578947)
## 42) startprice.log>=4.917424 107 32 N (0.70093458 0.29906542)
## 84) startprice.log< 5.005922 8 0 N (1.00000000 0.00000000) *
## 85) startprice.log>=5.005922 99 32 N (0.67676768 0.32323232)
## 170) startprice.log>=5.023794 84 24 N (0.71428571 0.28571429)
## 340) startprice.log< 5.135337 16 3 N (0.81250000 0.18750000) *
## 341) startprice.log>=5.135337 68 21 N (0.69117647 0.30882353)
## 682) startprice.log>=5.227067 43 11 N (0.74418605 0.25581395) *
## 683) startprice.log< 5.227067 25 10 N (0.60000000 0.40000000)
## 1366) startprice.log< 5.192818 14 4 N (0.71428571 0.28571429) *
## 1367) startprice.log>=5.192818 11 5 Y (0.45454545 0.54545455) *
## 171) startprice.log< 5.023794 15 7 Y (0.46666667 0.53333333) *
## 43) startprice.log< 4.917424 7 3 Y (0.42857143 0.57142857) *
## 11) startprice.log< 4.574498 66 28 N (0.57575758 0.42424242)
## 22) startprice.log< 3.365867 11 2 N (0.81818182 0.18181818) *
## 23) startprice.log>=3.365867 55 26 N (0.52727273 0.47272727)
## 46) startprice.log>=4.248424 35 13 N (0.62857143 0.37142857)
## 92) startprice.log< 4.375675 10 2 N (0.80000000 0.20000000) *
## 93) startprice.log>=4.375675 25 11 N (0.56000000 0.44000000)
## 186) startprice.log>=4.430386 18 7 N (0.61111111 0.38888889) *
## 187) startprice.log< 4.430386 7 3 Y (0.42857143 0.57142857) *
## 47) startprice.log< 4.248424 20 7 Y (0.35000000 0.65000000) *
## 3) biddable>=0.5 440 110 Y (0.25000000 0.75000000)
## 6) startprice.log>=4.923459 163 78 N (0.52147239 0.47852761)
## 12) startprice.log>=6.024113 21 3 N (0.85714286 0.14285714) *
## 13) startprice.log< 6.024113 142 67 Y (0.47183099 0.52816901)
## 26) startprice.log< 5.010535 9 2 N (0.77777778 0.22222222) *
## 27) startprice.log>=5.010535 133 60 Y (0.45112782 0.54887218)
## 54) startprice.log>=5.176086 104 50 Y (0.48076923 0.51923077)
## 108) startprice.log< 5.501177 43 17 N (0.60465116 0.39534884)
## 216) startprice.log>=5.322712 16 2 N (0.87500000 0.12500000) *
## 217) startprice.log< 5.322712 27 12 Y (0.44444444 0.55555556)
## 434) startprice.log< 5.220329 7 2 N (0.71428571 0.28571429) *
## 435) startprice.log>=5.220329 20 7 Y (0.35000000 0.65000000) *
## 109) startprice.log>=5.501177 61 24 Y (0.39344262 0.60655738)
## 218) startprice.log>=5.531362 46 21 Y (0.45652174 0.54347826)
## 436) startprice.log< 5.600228 9 2 N (0.77777778 0.22222222) *
## 437) startprice.log>=5.600228 37 14 Y (0.37837838 0.62162162) *
## 219) startprice.log< 5.531362 15 3 Y (0.20000000 0.80000000) *
## 55) startprice.log< 5.176086 29 10 Y (0.34482759 0.65517241) *
## 7) startprice.log< 4.923459 277 25 Y (0.09025271 0.90974729) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6578171
## 3 0.2 0.7653631
## 4 0.3 0.8025478
## 5 0.4 0.8030303
## 6 0.5 0.8013245
## 7 0.6 0.7820966
## 8 0.7 0.7106326
## 9 0.8 0.7106326
## 10 0.9 0.6923077
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 423
## 2 Y 80
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 102
## 2 371
## Prediction
## Reference N Y
## N 423 102
## Y 80 371
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.135246e-01 6.261780e-01 7.876443e-01 8.374971e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 5.319126e-73 1.195599e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6390244
## 3 0.2 0.7014028
## 4 0.3 0.7353952
## 5 0.4 0.7395349
## 6 0.5 0.7502987
## 7 0.6 0.7634961
## 8 0.7 0.7096774
## 9 0.8 0.7096774
## 10 0.9 0.7102526
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 404
## 2 Y 112
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 72
## 2 297
## Prediction
## Reference N Y
## N 404 72
## Y 112 297
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.920904e-01 5.788853e-01 7.638373e-01 8.183833e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 4.582845e-56 4.038763e-03
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.464 0.008
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8754662 0.4 0.8030303 0.8135246
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7876443 0.8374971 0.626178 0.8186805
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7634961 0.7920904
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7638373 0.8183833 0.5788853if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, startprice.log"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0111 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 976
##
## CP nsplit rel error
## 1 0.48780488 0 1.0000000
## 2 0.01662971 1 0.5121951
## 3 0.01108647 3 0.4789357
##
## Variable importance
## biddable startprice.log
## 57 43
##
## Node number 1: 976 observations, complexity param=0.4878049
## predicted class=N expected loss=0.4620902 P(node) =1
## class counts: 525 451
## probabilities: 0.538 0.462
## left son=2 (536 obs) right son=3 (440 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=132.8253, (0 missing)
## startprice.log < 4.610145 to the right, improve=113.3071, (0 missing)
## Surrogate splits:
## startprice.log < 5.030417 to the right, agree=0.738, adj=0.418, (0 split)
##
## Node number 2: 536 observations
## predicted class=N expected loss=0.2257463 P(node) =0.5491803
## class counts: 415 121
## probabilities: 0.774 0.226
##
## Node number 3: 440 observations, complexity param=0.01662971
## predicted class=Y expected loss=0.25 P(node) =0.4508197
## class counts: 110 330
## probabilities: 0.250 0.750
## left son=6 (163 obs) right son=7 (277 obs)
## Primary splits:
## startprice.log < 4.923459 to the right, improve=38.16294, (0 missing)
##
## Node number 6: 163 observations, complexity param=0.01662971
## predicted class=N expected loss=0.4785276 P(node) =0.1670082
## class counts: 85 78
## probabilities: 0.521 0.479
## left son=12 (21 obs) right son=13 (142 obs)
## Primary splits:
## startprice.log < 6.024113 to the right, improve=5.432188, (0 missing)
##
## Node number 7: 277 observations
## predicted class=Y expected loss=0.09025271 P(node) =0.2838115
## class counts: 25 252
## probabilities: 0.090 0.910
##
## Node number 12: 21 observations
## predicted class=N expected loss=0.1428571 P(node) =0.02151639
## class counts: 18 3
## probabilities: 0.857 0.143
##
## Node number 13: 142 observations
## predicted class=Y expected loss=0.471831 P(node) =0.1454918
## class counts: 67 75
## probabilities: 0.472 0.528
##
## n= 976
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 976 451 N (0.53790984 0.46209016)
## 2) biddable< 0.5 536 121 N (0.77425373 0.22574627) *
## 3) biddable>=0.5 440 110 Y (0.25000000 0.75000000)
## 6) startprice.log>=4.923459 163 78 N (0.52147239 0.47852761)
## 12) startprice.log>=6.024113 21 3 N (0.85714286 0.14285714) *
## 13) startprice.log< 6.024113 142 67 Y (0.47183099 0.52816901) *
## 7) startprice.log< 4.923459 277 25 Y (0.09025271 0.90974729) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6320953
## 3 0.2 0.6372688
## 4 0.3 0.7517241
## 5 0.4 0.7517241
## 6 0.5 0.7517241
## 7 0.6 0.6923077
## 8 0.7 0.6923077
## 9 0.8 0.6923077
## 10 0.9 0.6923077
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 433
## 2 Y 124
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 92
## 2 327
## Prediction
## Reference N Y
## N 433 92
## Y 124 327
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.786885e-01 5.525805e-01 7.513118e-01 8.043723e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 4.209745e-55 3.492018e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6321484
## 3 0.2 0.6311668
## 4 0.3 0.7705957
## 5 0.4 0.7705957
## 6 0.5 0.7705957
## 7 0.6 0.7102526
## 8 0.7 0.7102526
## 9 0.8 0.7102526
## 10 0.9 0.7102526
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 400
## 2 Y 105
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 76
## 2 304
## Prediction
## Reference N Y
## N 400 76
## Y 105 304
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.954802e-01 5.865393e-01 7.673772e-01 8.215999e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.262986e-57 3.741344e-02
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, startprice.log 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.013 0.012 0.8085735
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.7517241 0.7745729
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7513118 0.8043723 0.539325 0.8157399
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7705957 0.7954802
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7673772 0.8215999 0.5865393
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02352595 0.05361153# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.log"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9769 -0.7235 -0.5139 0.7920 2.0974
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.72869 0.48878 5.583 2.37e-08 ***
## biddable 1.68289 0.16366 10.283 < 2e-16 ***
## startprice.log -0.74275 0.09048 -8.209 2.23e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.41 on 975 degrees of freedom
## Residual deviance: 956.82 on 973 degrees of freedom
## AIC: 962.82
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6347643
## 3 0.2 0.7094536
## 4 0.3 0.7361963
## 5 0.4 0.7467249
## 6 0.5 0.7471526
## 7 0.6 0.7211896
## 8 0.7 0.6740638
## 9 0.8 0.5185185
## 10 0.9 0.3669065
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 426
## 2 Y 123
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 99
## 2 328
## Prediction
## Reference N Y
## N 426 99
## Y 123 328
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.725410e-01 5.407292e-01 7.449290e-01 7.984977e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 2.909621e-52 1.226710e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6341085
## 3 0.2 0.6978485
## 4 0.3 0.7425968
## 5 0.4 0.7701711
## 6 0.5 0.7692308
## 7 0.6 0.7530364
## 8 0.7 0.7005988
## 9 0.8 0.5580589
## 10 0.9 0.4200385
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 382
## 2 Y 94
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 94
## 2 315
## Prediction
## Reference N Y
## N 382 94
## Y 94 315
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875706e-01 5.726922e-01 7.591217e-01 8.140900e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 5.004042e-54 1.000000e+00
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm biddable, startprice.log 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.94 0.011 0.8349826
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.7471526 0.7694604
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.744929 0.7984977 0.5343223 0.8432177
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7701711 0.7875706
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7591217 0.81409 0.5726922 962.8176
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.006501391 0.01185686if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.log, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0992 -0.7121 -0.5102 0.7391 2.1050
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.80667 0.49288 5.694 1.24e-08
## biddable 3.17394 1.61551 1.965 0.049453
## startprice.log -0.75755 0.09129 -8.298 < 2e-16
## `biddable:D.terms.n.post.stop` 0.48909 0.32660 1.498 0.134253
## `biddable:D.TfIdf.sum.post.stem` 0.34883 0.21771 1.602 0.109093
## `biddable:D.ratio.nstopwrds.nwrds` -1.20995 1.60444 -0.754 0.450773
## `biddable:D.npnct06.log` -0.25310 0.84377 -0.300 0.764208
## `biddable:D.nchrs.log` 0.58602 1.09548 0.535 0.592692
## `biddable:D.nwrds.unq.log` -4.38399 3.56250 -1.231 0.218476
## `biddable:cellular.fctr1` -0.23892 0.28879 -0.827 0.408050
## `biddable:cellular.fctrUnknown` -1.28296 0.36910 -3.476 0.000509
##
## (Intercept) ***
## biddable *
## startprice.log ***
## `biddable:D.terms.n.post.stop`
## `biddable:D.TfIdf.sum.post.stem`
## `biddable:D.ratio.nstopwrds.nwrds`
## `biddable:D.npnct06.log`
## `biddable:D.nchrs.log`
## `biddable:D.nwrds.unq.log`
## `biddable:cellular.fctr1`
## `biddable:cellular.fctrUnknown` ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.41 on 975 degrees of freedom
## Residual deviance: 939.83 on 965 degrees of freedom
## AIC: 961.83
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6356589
## 3 0.2 0.7100694
## 4 0.3 0.7394439
## 5 0.4 0.7461024
## 6 0.5 0.7465116
## 7 0.6 0.7348952
## 8 0.7 0.6919946
## 9 0.8 0.5466035
## 10 0.9 0.3879004
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 437
## 2 Y 130
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 88
## 2 321
## Prediction
## Reference N Y
## N 437 88
## Y 130 321
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.766393e-01 5.477275e-01 7.491833e-01 8.024150e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 3.810422e-54 5.488506e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6350932
## 3 0.2 0.6985019
## 4 0.3 0.7428571
## 5 0.4 0.7699877
## 6 0.5 0.7639594
## 7 0.6 0.7405405
## 8 0.7 0.6960352
## 9 0.8 0.5655877
## 10 0.9 0.4423440
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 385
## 2 Y 96
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 91
## 2 313
## Prediction
## Reference N Y
## N 385 91
## Y 96 313
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.887006e-01 5.745991e-01 7.603001e-01 8.151638e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.563801e-54 7.698975e-01
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 biddable, startprice.log, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.963 0.015
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.839679 0.5 0.7465116 0.7704987
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7491833 0.802415 0.5354264 0.8384587
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7699877 0.7887006
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7603001 0.8151638 0.5745991 961.8295
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005998662 0.01291382# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: biddable, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4271 -0.7165 -0.3083 0.6711 2.5888
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -5.382e-02 5.261e+00 -0.010
## biddable 1.478e+00 1.877e-01 7.876
## D.npnct15.log 1.366e+00 7.556e-01 1.808
## D.T.excel 1.558e-01 4.397e-01 0.354
## D.npnct03.log 8.826e-01 1.574e+00 0.561
## D.T.screen -5.518e-02 6.152e-01 -0.090
## D.terms.n.stem.stop.Ratio 1.960e-01 4.602e+00 0.043
## D.T.use 8.838e-01 5.558e-01 1.590
## D.ratio.sum.TfIdf.nwrds 2.653e-01 2.323e-01 1.142
## D.T.great 3.166e-01 5.813e-01 0.545
## D.npnct01.log 2.352e-01 5.127e-01 0.459
## D.T.good 5.483e-01 6.329e-01 0.866
## D.TfIdf.sum.stem.stop.Ratio 3.218e+00 2.993e+00 1.075
## D.T.scratch 1.170e+00 6.727e-01 1.740
## D.P.air 1.666e-01 1.066e+00 0.156
## storage.fctr16 5.544e-01 5.021e-01 1.104
## storage.fctr32 4.555e-01 5.274e-01 0.864
## storage.fctr64 1.078e+00 5.159e-01 2.090
## storage.fctrUnknown 6.445e-01 6.801e-01 0.948
## D.P.mini 2.298e-01 8.102e-01 0.284
## D.T.work -2.843e-01 7.461e-01 -0.381
## D.T.ipad -3.721e-01 7.321e-01 -0.508
## D.npnct11.log 1.167e-01 3.177e-01 0.367
## .rnorm -1.175e-01 8.908e-02 -1.319
## D.npnct10.log 9.028e-01 1.251e+00 0.722
## D.T.condit -1.170e+00 6.802e-01 -1.720
## D.T.new -3.155e-01 4.396e-01 -0.718
## D.npnct08.log -8.014e-01 7.048e-01 -1.137
## `prdline.my.fctriPad 1` 4.966e-01 4.541e-01 1.093
## `prdline.my.fctriPad 2` 7.061e-01 4.543e-01 1.554
## `prdline.my.fctriPad 3+` 5.641e-01 4.370e-01 1.291
## prdline.my.fctriPadAir 1.233e+00 4.513e-01 2.733
## prdline.my.fctriPadmini 7.128e-01 4.300e-01 1.658
## `prdline.my.fctriPadmini 2+` 8.789e-01 4.764e-01 1.845
## color.fctrBlack -1.238e-01 2.475e-01 -0.500
## color.fctrGold -1.929e-01 4.908e-01 -0.393
## `color.fctrSpace Gray` 1.357e-01 3.073e-01 0.442
## color.fctrWhite 1.416e-01 2.389e-01 0.592
## D.npnct06.log -1.434e+00 1.087e+00 -1.319
## D.npnct28.log -3.043e+00 6.674e+02 -0.005
## D.npnct12.log 1.167e-01 6.933e-01 0.168
## D.npnct09.log -7.648e+00 3.504e+02 -0.022
## D.ndgts.log 3.472e-01 3.990e-01 0.870
## cellular.fctr1 9.051e-02 2.047e-01 0.442
## cellular.fctrUnknown -2.866e-01 4.279e-01 -0.670
## D.npnct14.log -7.643e-01 7.284e-01 -1.049
## D.terms.n.post.stop -3.795e-02 3.651e-02 -1.039
## D.npnct05.log -2.819e+00 1.675e+00 -1.682
## `condition.fctrFor parts or not working` -3.555e-01 3.904e-01 -0.911
## `condition.fctrManufacturer refurbished` -1.963e-01 6.507e-01 -0.302
## condition.fctrNew -4.289e-01 2.936e-01 -1.461
## `condition.fctrNew other (see details)` -2.950e-01 4.065e-01 -0.726
## `condition.fctrSeller refurbished` -8.835e-01 4.464e-01 -1.979
## idseq.my -5.767e-04 1.925e-04 -2.995
## startprice.log -9.601e-01 1.369e-01 -7.015
## Pr(>|z|)
## (Intercept) 0.99184
## biddable 3.38e-15 ***
## D.npnct15.log 0.07054 .
## D.T.excel 0.72303
## D.npnct03.log 0.57502
## D.T.screen 0.92852
## D.terms.n.stem.stop.Ratio 0.96603
## D.T.use 0.11181
## D.ratio.sum.TfIdf.nwrds 0.25335
## D.T.great 0.58600
## D.npnct01.log 0.64635
## D.T.good 0.38634
## D.TfIdf.sum.stem.stop.Ratio 0.28225
## D.T.scratch 0.08187 .
## D.P.air 0.87583
## storage.fctr16 0.26959
## storage.fctr32 0.38774
## storage.fctr64 0.03662 *
## storage.fctrUnknown 0.34331
## D.P.mini 0.77669
## D.T.work 0.70314
## D.T.ipad 0.61128
## D.npnct11.log 0.71350
## .rnorm 0.18705
## D.npnct10.log 0.47035
## D.T.condit 0.08535 .
## D.T.new 0.47284
## D.npnct08.log 0.25548
## `prdline.my.fctriPad 1` 0.27418
## `prdline.my.fctriPad 2` 0.12016
## `prdline.my.fctriPad 3+` 0.19679
## prdline.my.fctriPadAir 0.00629 **
## prdline.my.fctriPadmini 0.09735 .
## `prdline.my.fctriPadmini 2+` 0.06505 .
## color.fctrBlack 0.61702
## color.fctrGold 0.69430
## `color.fctrSpace Gray` 0.65880
## color.fctrWhite 0.55357
## D.npnct06.log 0.18729
## D.npnct28.log 0.99636
## D.npnct12.log 0.86629
## D.npnct09.log 0.98259
## D.ndgts.log 0.38426
## cellular.fctr1 0.65845
## cellular.fctrUnknown 0.50301
## D.npnct14.log 0.29401
## D.terms.n.post.stop 0.29858
## D.npnct05.log 0.09250 .
## `condition.fctrFor parts or not working` 0.36244
## `condition.fctrManufacturer refurbished` 0.76293
## condition.fctrNew 0.14409
## `condition.fctrNew other (see details)` 0.46794
## `condition.fctrSeller refurbished` 0.04781 *
## idseq.my 0.00274 **
## startprice.log 2.29e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.41 on 975 degrees of freedom
## Residual deviance: 870.61 on 921 degrees of freedom
## AIC: 980.61
##
## Number of Fisher Scoring iterations: 13
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6748092
## 3 0.2 0.7169150
## 4 0.3 0.7586207
## 5 0.4 0.7720829
## 6 0.5 0.7808858
## 7 0.6 0.7652174
## 8 0.7 0.7231788
## 9 0.8 0.6176912
## 10 0.9 0.4236111
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 453
## 2 Y 116
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 72
## 2 335
## Prediction
## Reference N Y
## N 453 72
## Y 116 335
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.073770e-01 6.098441e-01 7.812112e-01 8.316731e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 1.309621e-69 1.712124e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6677995
## 3 0.2 0.7176471
## 4 0.3 0.7370737
## 5 0.4 0.7580453
## 6 0.5 0.7623888
## 7 0.6 0.7594595
## 8 0.7 0.7209302
## 9 0.8 0.6276423
## 10 0.9 0.4798535
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 398
## 2 Y 109
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 78
## 2 300
## Prediction
## Reference N Y
## N 398 78
## Y 109 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.887006e-01 5.726857e-01 7.603001e-01 8.151638e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.563801e-54 2.824855e-02
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 biddable, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.176 0.104
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8658642 0.5 0.7808858 0.760236
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7812112 0.8316731 0.5157767 0.8449025
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7623888 0.7887006
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7603001 0.8151638 0.5726857 980.6074
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01365954 0.02754959rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 68.280 93.261 24.981
## 11 fit.models 7 1 93.262 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 97.402 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
#stop(here")
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 97.402 97.423 0.021
## 2 fit.models_1_glm 2 0 97.423 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.6377 -0.6935 -0.2411 0.6476 2.5432
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 7.849e+01 4.361e+01 1.800
## biddable 1.471e+00 1.927e-01 7.630
## D.ratio.nstopwrds.nwrds -1.693e+01 7.189e+00 -2.354
## D.npnct15.log 7.303e-01 8.182e-01 0.893
## D.T.excel 4.722e-01 4.881e-01 0.967
## D.npnct03.log 7.286e-01 1.863e+00 0.391
## D.T.screen 1.693e-01 7.190e-01 0.235
## D.terms.n.stem.stop.Ratio -6.612e+01 4.118e+01 -1.606
## D.T.use 1.391e+00 6.958e-01 1.999
## D.ratio.sum.TfIdf.nwrds 7.753e-01 7.548e-01 1.027
## D.T.great 7.095e-01 6.225e-01 1.140
## D.npnct01.log -3.938e-01 6.716e-01 -0.586
## D.T.good 9.181e-01 7.018e-01 1.308
## D.TfIdf.sum.stem.stop.Ratio 8.504e+00 1.943e+01 0.438
## D.T.scratch 1.802e+00 8.196e-01 2.199
## D.P.air 8.713e-01 1.248e+00 0.698
## storage.fctr16 2.378e-01 5.194e-01 0.458
## storage.fctr32 2.767e-02 5.461e-01 0.051
## storage.fctr64 8.125e-01 5.308e-01 1.531
## storage.fctrUnknown 2.494e-01 7.004e-01 0.356
## D.P.mini 1.954e-01 8.010e-01 0.244
## D.T.work 2.042e-01 7.993e-01 0.256
## D.T.ipad -3.611e-01 8.250e-01 -0.438
## D.npnct11.log -5.341e-02 3.864e-01 -0.138
## .rnorm -1.036e-01 9.124e-02 -1.136
## D.npnct10.log 1.767e-02 1.515e+00 0.012
## D.TfIdf.sum.post.stop 1.123e+00 2.959e+00 0.380
## D.TfIdf.sum.post.stem -7.680e-01 3.083e+00 -0.249
## D.sum.TfIdf NA NA NA
## D.npnct13.log -4.918e-02 4.191e-01 -0.117
## D.T.condit 4.162e-01 1.043e+00 0.399
## D.T.new -1.722e-01 6.404e-01 -0.269
## D.npnct08.log -7.515e-01 7.768e-01 -0.967
## `prdline.my.fctriPad 1` 4.138e-01 4.660e-01 0.888
## `prdline.my.fctriPad 2` 6.081e-01 4.718e-01 1.289
## `prdline.my.fctriPad 3+` 4.419e-01 4.515e-01 0.979
## prdline.my.fctriPadAir 1.168e+00 4.639e-01 2.517
## prdline.my.fctriPadmini 5.574e-01 4.439e-01 1.256
## `prdline.my.fctriPadmini 2+` 8.415e-01 4.932e-01 1.706
## color.fctrBlack -1.571e-01 2.554e-01 -0.615
## color.fctrGold -3.127e-01 5.010e-01 -0.624
## `color.fctrSpace Gray` 1.696e-01 3.205e-01 0.529
## color.fctrWhite 9.751e-02 2.469e-01 0.395
## D.npnct16.log 1.012e+00 2.498e+00 0.405
## D.npnct24.log -1.282e+01 8.748e+00 -1.465
## D.nstopwrds.log 4.296e+00 2.172e+00 1.978
## D.npnct06.log -2.086e+00 2.808e+00 -0.743
## D.npnct28.log -2.927e+00 1.100e+03 -0.003
## D.nuppr.log -4.728e+00 5.855e+00 -0.807
## D.npnct12.log 5.770e-01 7.779e-01 0.742
## D.nchrs.log 3.188e+00 6.726e+00 0.474
## D.nwrds.log 5.364e-01 3.257e+00 0.165
## D.npnct09.log -8.613e+00 5.767e+02 -0.015
## D.ndgts.log -4.734e-01 5.623e-01 -0.842
## D.nwrds.unq.log -3.224e+00 3.807e+00 -0.847
## `carrier.fctrAT&T` 3.238e-01 6.649e-01 0.487
## carrier.fctrOther 1.542e+01 8.330e+02 0.019
## carrier.fctrSprint 7.366e-01 9.023e-01 0.816
## `carrier.fctrT-Mobile` -1.554e+00 1.344e+00 -1.156
## carrier.fctrUnknown -2.862e-01 4.413e-01 -0.649
## carrier.fctrVerizon 8.956e-01 7.079e-01 1.265
## cellular.fctr1 -2.059e-01 6.100e-01 -0.338
## cellular.fctrUnknown NA NA NA
## D.npnct14.log -1.428e+00 8.443e-01 -1.691
## D.terms.n.post.stem 7.631e+00 4.370e+00 1.746
## D.terms.n.post.stop -7.645e+00 4.327e+00 -1.767
## D.npnct05.log -1.914e+00 1.875e+00 -1.020
## `condition.fctrFor parts or not working` -3.578e-01 4.006e-01 -0.893
## `condition.fctrManufacturer refurbished` -4.505e-01 6.870e-01 -0.656
## condition.fctrNew -3.619e-01 3.005e-01 -1.204
## `condition.fctrNew other (see details)` -5.057e-01 4.491e-01 -1.126
## `condition.fctrSeller refurbished` -8.772e-01 4.604e-01 -1.905
## idseq.my -6.635e-04 1.978e-04 -3.355
## startprice.log -9.830e-01 1.339e-01 -7.340
## Pr(>|z|)
## (Intercept) 0.071878 .
## biddable 2.35e-14 ***
## D.ratio.nstopwrds.nwrds 0.018550 *
## D.npnct15.log 0.372048
## D.T.excel 0.333407
## D.npnct03.log 0.695736
## D.T.screen 0.813885
## D.terms.n.stem.stop.Ratio 0.108341
## D.T.use 0.045652 *
## D.ratio.sum.TfIdf.nwrds 0.304379
## D.T.great 0.254373
## D.npnct01.log 0.557619
## D.T.good 0.190785
## D.TfIdf.sum.stem.stop.Ratio 0.661550
## D.T.scratch 0.027870 *
## D.P.air 0.484905
## storage.fctr16 0.647056
## storage.fctr32 0.959589
## storage.fctr64 0.125834
## storage.fctrUnknown 0.721788
## D.P.mini 0.807303
## D.T.work 0.798332
## D.T.ipad 0.661570
## D.npnct11.log 0.890079
## .rnorm 0.256045
## D.npnct10.log 0.990697
## D.TfIdf.sum.post.stop 0.704217
## D.TfIdf.sum.post.stem 0.803286
## D.sum.TfIdf NA
## D.npnct13.log 0.906601
## D.T.condit 0.689817
## D.T.new 0.787941
## D.npnct08.log 0.333340
## `prdline.my.fctriPad 1` 0.374571
## `prdline.my.fctriPad 2` 0.197449
## `prdline.my.fctriPad 3+` 0.327754
## prdline.my.fctriPadAir 0.011825 *
## prdline.my.fctriPadmini 0.209222
## `prdline.my.fctriPadmini 2+` 0.087941 .
## color.fctrBlack 0.538454
## color.fctrGold 0.532507
## `color.fctrSpace Gray` 0.596693
## color.fctrWhite 0.692839
## D.npnct16.log 0.685456
## D.npnct24.log 0.142847
## D.nstopwrds.log 0.047923 *
## D.npnct06.log 0.457566
## D.npnct28.log 0.997876
## D.nuppr.log 0.419396
## D.npnct12.log 0.458237
## D.nchrs.log 0.635512
## D.nwrds.log 0.869194
## D.npnct09.log 0.988084
## D.ndgts.log 0.399872
## D.nwrds.unq.log 0.397055
## `carrier.fctrAT&T` 0.626222
## carrier.fctrOther 0.985231
## carrier.fctrSprint 0.414290
## `carrier.fctrT-Mobile` 0.247753
## carrier.fctrUnknown 0.516603
## carrier.fctrVerizon 0.205786
## cellular.fctr1 0.735656
## cellular.fctrUnknown NA
## D.npnct14.log 0.090852 .
## D.terms.n.post.stem 0.080725 .
## D.terms.n.post.stop 0.077267 .
## D.npnct05.log 0.307511
## `condition.fctrFor parts or not working` 0.371786
## `condition.fctrManufacturer refurbished` 0.511963
## condition.fctrNew 0.228507
## `condition.fctrNew other (see details)` 0.260097
## `condition.fctrSeller refurbished` 0.056764 .
## idseq.my 0.000794 ***
## startprice.log 2.13e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.4 on 975 degrees of freedom
## Residual deviance: 841.1 on 904 degrees of freedom
## AIC: 985.1
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6847826
## 3 0.2 0.7339286
## 4 0.3 0.7690783
## 5 0.4 0.7839305
## 6 0.5 0.7847222
## 7 0.6 0.7803681
## 8 0.7 0.7408377
## 9 0.8 0.6360947
## 10 0.9 0.4662162
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 451 74
## 2 Y 112 339
## Prediction
## Reference N Y
## N 451 74
## Y 112 339
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.094262e-01 6.143587e-01 7.833545e-01 8.336156e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 9.959402e-71 6.668282e-03
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6764200
## 3 0.2 0.7096135
## 4 0.3 0.7409836
## 5 0.4 0.7568209
## 6 0.5 0.7575000
## 7 0.6 0.7412399
## 8 0.7 0.7153076
## 9 0.8 0.6466877
## 10 0.9 0.4863884
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 388 88
## 2 Y 106 303
## Prediction
## Reference N Y
## N 388 88
## Y 106 303
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.807910e-01 5.576847e-01 7.520575e-01 8.076410e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 4.669336e-51 2.222645e-01
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.311 0.175
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8761525 0.5 0.7847222 0.7633192
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7833545 0.8336156 0.5226601 0.8391188
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7575 0.780791
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7520575 0.807641 0.5576847 985.1018
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0004195939 0.001563689
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 97.423 102.61 5.187
## 3 fit.models_1_bayesglm 3 0 102.611 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
## Loading required package: lme4
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.5546 -0.7146 -0.2888 0.6735 2.5740
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 0.362188 6.194353 0.058
## biddable 1.459201 0.187819 7.769
## D.ratio.nstopwrds.nwrds -0.912835 2.327208 -0.392
## D.npnct15.log 1.182152 0.774323 1.527
## D.T.excel 0.325396 0.444681 0.732
## D.npnct03.log 0.445299 1.573678 0.283
## D.T.screen 0.100009 0.658937 0.152
## D.terms.n.stem.stop.Ratio 1.322235 4.987976 0.265
## D.T.use 1.273897 0.622405 2.047
## D.ratio.sum.TfIdf.nwrds 0.245754 0.393325 0.625
## D.T.great 0.564651 0.579705 0.974
## D.npnct01.log 0.064311 0.522672 0.123
## D.T.good 0.806453 0.652268 1.236
## D.TfIdf.sum.stem.stop.Ratio 3.163707 3.422420 0.924
## D.T.scratch 1.221956 0.740082 1.651
## D.P.air 0.417381 0.944358 0.442
## storage.fctr16 0.180754 0.446232 0.405
## storage.fctr32 0.036677 0.470277 0.078
## storage.fctr64 0.767275 0.460265 1.667
## storage.fctrUnknown 0.237572 0.608124 0.391
## D.P.mini 0.180390 0.775364 0.233
## D.T.work 0.025937 0.758149 0.034
## D.T.ipad -0.264041 0.772145 -0.342
## D.npnct11.log 0.139202 0.331633 0.420
## .rnorm -0.104321 0.089190 -1.170
## D.npnct10.log 0.870521 1.336571 0.651
## D.TfIdf.sum.post.stop 0.101969 0.300044 0.340
## D.TfIdf.sum.post.stem 0.108831 0.314361 0.346
## D.sum.TfIdf 0.108831 0.314361 0.346
## D.npnct13.log 0.093362 0.326867 0.286
## D.T.condit -0.287142 0.835637 -0.344
## D.T.new 0.051463 0.492242 0.105
## D.npnct08.log -0.626571 0.723805 -0.866
## `prdline.my.fctriPad 1` 0.312114 0.420426 0.742
## `prdline.my.fctriPad 2` 0.517839 0.421102 1.230
## `prdline.my.fctriPad 3+` 0.397678 0.400117 0.994
## prdline.my.fctriPadAir 1.057981 0.414317 2.554
## prdline.my.fctriPadmini 0.498316 0.397081 1.255
## `prdline.my.fctriPadmini 2+` 0.737309 0.442571 1.666
## color.fctrBlack -0.143274 0.245663 -0.583
## color.fctrGold -0.235276 0.473132 -0.497
## `color.fctrSpace Gray` 0.148538 0.307330 0.483
## color.fctrWhite 0.093593 0.237942 0.393
## D.npnct16.log 0.962781 1.911758 0.504
## D.npnct24.log -2.798740 2.971471 -0.942
## D.nstopwrds.log 0.709490 0.655993 1.082
## D.npnct06.log -2.085277 2.144558 -0.972
## D.npnct28.log -0.066478 2.180954 -0.030
## D.nuppr.log -0.152886 0.520744 -0.294
## D.npnct12.log 0.380989 0.709389 0.537
## D.nchrs.log -0.135032 0.501679 -0.269
## D.nwrds.log 0.141627 0.767827 0.184
## D.npnct09.log -1.984417 5.160049 -0.385
## D.ndgts.log 0.207059 0.398557 0.520
## D.nwrds.unq.log -0.360338 0.989602 -0.364
## `carrier.fctrAT&T` 0.068465 0.761603 0.090
## carrier.fctrOther 1.576461 1.715817 0.919
## carrier.fctrSprint 0.428862 0.907203 0.473
## `carrier.fctrT-Mobile` -1.391579 1.134562 -1.227
## carrier.fctrUnknown -0.578856 0.777938 -0.744
## carrier.fctrVerizon 0.497831 0.785960 0.633
## cellular.fctr1 0.057840 0.738385 0.078
## cellular.fctrUnknown 0.268591 0.846865 0.317
## D.npnct14.log -0.944405 0.746779 -1.265
## D.terms.n.post.stem -0.027060 0.189384 -0.143
## D.terms.n.post.stop -0.061837 0.188582 -0.328
## D.npnct05.log -2.118955 1.443672 -1.468
## `condition.fctrFor parts or not working` -0.325231 0.383239 -0.849
## `condition.fctrManufacturer refurbished` -0.301912 0.628863 -0.480
## condition.fctrNew -0.361606 0.293146 -1.234
## `condition.fctrNew other (see details)` -0.446502 0.418412 -1.067
## `condition.fctrSeller refurbished` -0.786494 0.433186 -1.816
## idseq.my -0.000648 0.000192 -3.375
## startprice.log -0.959022 0.133142 -7.203
## Pr(>|z|)
## (Intercept) 0.953374
## biddable 7.90e-15 ***
## D.ratio.nstopwrds.nwrds 0.694877
## D.npnct15.log 0.126838
## D.T.excel 0.464321
## D.npnct03.log 0.777202
## D.T.screen 0.879366
## D.terms.n.stem.stop.Ratio 0.790944
## D.T.use 0.040684 *
## D.ratio.sum.TfIdf.nwrds 0.532095
## D.T.great 0.330041
## D.npnct01.log 0.902073
## D.T.good 0.216316
## D.TfIdf.sum.stem.stop.Ratio 0.355275
## D.T.scratch 0.098716 .
## D.P.air 0.658509
## storage.fctr16 0.685427
## storage.fctr32 0.937837
## storage.fctr64 0.095509 .
## storage.fctrUnknown 0.696045
## D.P.mini 0.816032
## D.T.work 0.972709
## D.T.ipad 0.732383
## D.npnct11.log 0.674671
## .rnorm 0.242138
## D.npnct10.log 0.514847
## D.TfIdf.sum.post.stop 0.733973
## D.TfIdf.sum.post.stem 0.729193
## D.sum.TfIdf 0.729193
## D.npnct13.log 0.775164
## D.T.condit 0.731132
## D.T.new 0.916735
## D.npnct08.log 0.386675
## `prdline.my.fctriPad 1` 0.457860
## `prdline.my.fctriPad 2` 0.218801
## `prdline.my.fctriPad 3+` 0.320270
## prdline.my.fctriPadAir 0.010663 *
## prdline.my.fctriPadmini 0.209498
## `prdline.my.fctriPadmini 2+` 0.095720 .
## color.fctrBlack 0.559749
## color.fctrGold 0.618996
## `color.fctrSpace Gray` 0.628869
## color.fctrWhite 0.694066
## D.npnct16.log 0.614535
## D.npnct24.log 0.346259
## D.nstopwrds.log 0.279452
## D.npnct06.log 0.330873
## D.npnct28.log 0.975683
## D.nuppr.log 0.769069
## D.npnct12.log 0.591222
## D.nchrs.log 0.787806
## D.nwrds.log 0.853659
## D.npnct09.log 0.700554
## D.ndgts.log 0.603397
## D.nwrds.unq.log 0.715765
## `carrier.fctrAT&T` 0.928370
## carrier.fctrOther 0.358210
## carrier.fctrSprint 0.636406
## `carrier.fctrT-Mobile` 0.219998
## carrier.fctrUnknown 0.456822
## carrier.fctrVerizon 0.526469
## cellular.fctr1 0.937563
## cellular.fctrUnknown 0.751122
## D.npnct14.log 0.206001
## D.terms.n.post.stem 0.886382
## D.terms.n.post.stop 0.742981
## D.npnct05.log 0.142171
## `condition.fctrFor parts or not working` 0.396082
## `condition.fctrManufacturer refurbished` 0.631162
## condition.fctrNew 0.217376
## `condition.fctrNew other (see details)` 0.285911
## `condition.fctrSeller refurbished` 0.069431 .
## idseq.my 0.000739 ***
## startprice.log 5.89e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.41 on 975 degrees of freedom
## Residual deviance: 852.22 on 902 degrees of freedom
## AIC: 1000.2
##
## Number of Fisher Scoring iterations: 17
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6784074
## 3 0.2 0.7223199
## 4 0.3 0.7655786
## 5 0.4 0.7787419
## 6 0.5 0.7819026
## 7 0.6 0.7722772
## 8 0.7 0.7332457
## 9 0.8 0.6156156
## 10 0.9 0.4398625
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 451
## 2 Y 114
## sold.fctr.predict.All.X.bayesglm.Y
## 1 74
## 2 337
## Prediction
## Reference N Y
## N 451 74
## Y 114 337
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.073770e-01 6.100895e-01 7.812112e-01 8.316731e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 1.309621e-69 4.449971e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6660988
## 3 0.2 0.7106299
## 4 0.3 0.7420417
## 5 0.4 0.7541766
## 6 0.5 0.7614213
## 7 0.6 0.7530696
## 8 0.7 0.7120116
## 9 0.8 0.6453674
## 10 0.9 0.4770642
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 397
## 2 Y 109
## sold.fctr.predict.All.X.bayesglm.Y
## 1 79
## 2 300
## Prediction
## Reference N Y
## N 397 79
## Y 109 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875706e-01 5.704749e-01 7.591217e-01 8.140900e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 5.004042e-54 3.442634e-02
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.156 0.298
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8725837 0.5 0.7819026 0.7684379
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7812112 0.8316731 0.5326115 0.8410552
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7614213 0.7875706
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7591217 0.81409 0.5704749 1000.216
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007315812 0.01503357
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 102.611 107.887 5.277
## 4 fit.models_1_glmnet 4 0 107.888 NA NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log"## Loading required package: glmnet
## Loaded glmnet 2.0-2
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0522 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 7300 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 73 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable idseq.my startprice.log
## 9.552277e-01 1.309794e+00 -9.792281e-05 -3.393767e-01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.001351e+01
## biddable
## 1.473092e+00
## D.ratio.nstopwrds.nwrds
## -1.291936e+01
## D.npnct15.log
## 1.008154e+00
## D.T.excel
## 4.698188e-01
## D.npnct03.log
## 6.691643e-01
## D.T.screen
## 2.502725e-01
## D.terms.n.stem.stop.Ratio
## 2.772960e+00
## D.T.use
## 1.448813e+00
## D.ratio.sum.TfIdf.nwrds
## 6.969876e-01
## D.T.great
## 7.350167e-01
## D.npnct01.log
## -1.646351e-01
## D.T.good
## 1.040787e+00
## D.TfIdf.sum.stem.stop.Ratio
## 4.016821e+00
## D.T.scratch
## 1.570324e+00
## D.P.air
## 9.079118e-01
## storage.fctr16
## 2.353969e-01
## storage.fctr32
## 5.829740e-02
## storage.fctr64
## 8.320905e-01
## storage.fctrUnknown
## 2.673229e-01
## D.P.mini
## 2.175160e-01
## D.T.work
## 1.333821e-01
## D.T.ipad
## -2.889694e-01
## D.npnct11.log
## 2.430051e-02
## .rnorm
## -1.104180e-01
## D.npnct10.log
## 5.709954e-01
## D.TfIdf.sum.post.stop
## 4.283266e-01
## D.TfIdf.sum.post.stem
## 1.957567e-05
## D.npnct13.log
## 1.946778e-03
## D.T.condit
## 3.568606e-01
## D.T.new
## 1.332488e-01
## D.npnct08.log
## -6.523668e-01
## prdline.my.fctriPad 1
## 4.062081e-01
## prdline.my.fctriPad 2
## 6.217290e-01
## prdline.my.fctriPad 3+
## 4.776567e-01
## prdline.my.fctriPadAir
## 1.178548e+00
## prdline.my.fctriPadmini
## 5.918282e-01
## prdline.my.fctriPadmini 2+
## 8.616305e-01
## color.fctrBlack
## -1.591248e-01
## color.fctrGold
## -2.753249e-01
## color.fctrSpace Gray
## 1.506299e-01
## color.fctrWhite
## 9.541182e-02
## D.npnct16.log
## 9.951767e-01
## D.npnct24.log
## -1.241272e+01
## D.nstopwrds.log
## 3.520951e+00
## D.npnct06.log
## -2.225167e+00
## D.npnct28.log
## -6.618579e-01
## D.nuppr.log
## -1.068755e+00
## D.npnct12.log
## 5.228279e-01
## D.nwrds.log
## 3.870478e-01
## D.npnct09.log
## -3.213221e+00
## D.ndgts.log
## -1.262778e-01
## D.nwrds.unq.log
## -1.730880e+00
## carrier.fctrAT&T
## 1.387528e-01
## carrier.fctrOther
## 7.539458e+00
## carrier.fctrSprint
## 5.467951e-01
## carrier.fctrT-Mobile
## -1.665228e+00
## carrier.fctrUnknown
## -5.168876e-01
## carrier.fctrVerizon
## 6.030217e-01
## cellular.fctrUnknown
## 2.600047e-01
## D.npnct14.log
## -1.075374e+00
## D.terms.n.post.stem
## 5.568149e-02
## D.terms.n.post.stop
## -2.263768e-01
## D.npnct05.log
## -2.051269e+00
## condition.fctrFor parts or not working
## -3.901033e-01
## condition.fctrManufacturer refurbished
## -4.016063e-01
## condition.fctrNew
## -3.741282e-01
## condition.fctrNew other (see details)
## -5.502636e-01
## condition.fctrSeller refurbished
## -8.762562e-01
## idseq.my
## -6.532339e-04
## startprice.log
## -9.747200e-01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6320953
## 3 0.2 0.6387509
## 4 0.3 0.7361111
## 5 0.4 0.7395143
## 6 0.5 0.7435610
## 7 0.6 0.7289003
## 8 0.7 0.5073650
## 9 0.8 0.3497268
## 10 0.9 0.2244094
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 415
## 2 Y 119
## sold.fctr.predict.All.X.glmnet.Y
## 1 110
## 2 332
## Prediction
## Reference N Y
## N 415 110
## Y 119 332
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.653689e-01 5.273598e-01 7.374925e-01 7.916339e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 4.593282e-49 5.970449e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6321484
## 3 0.2 0.6365055
## 4 0.3 0.7367281
## 5 0.4 0.7675277
## 6 0.5 0.7721046
## 7 0.6 0.7548209
## 8 0.7 0.5489510
## 9 0.8 0.4015595
## 10 0.9 0.2370690
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 392
## 2 Y 99
## sold.fctr.predict.All.X.glmnet.Y
## 1 84
## 2 310
## Prediction
## Reference N Y
## N 392 84
## Y 99 310
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.932203e-01 5.829804e-01 7.650169e-01 8.194558e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.393813e-56 3.007110e-01
## model_id model_method
## 1 All.X.glmnet glmnet
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 6.821 1.94
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8401225 0.5 0.743561 0.769473
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7374925 0.7916339 0.5352804 0.8482361
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7721046 0.7932203
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7650169 0.8194558 0.5829804
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007872477 0.01795075
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_glmnet 4 0 107.888 118.572 10.684
## 5 fit.models_1_rpart 5 0 118.573 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0111 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 976
##
## CP nsplit rel error
## 1 0.48780488 0 1.0000000
## 2 0.04434590 1 0.5121951
## 3 0.01108647 3 0.4235033
##
## Variable importance
## biddable
## 43
## startprice.log
## 31
## idseq.my
## 14
## prdline.my.fctriPadAir
## 2
## condition.fctrFor parts or not working
## 2
## condition.fctrNew
## 2
## color.fctrSpace Gray
## 1
## prdline.my.fctriPad 2
## 1
## prdline.my.fctriPadmini 2+
## 1
## D.npnct03.log
## 1
## condition.fctrNew other (see details)
## 1
## storage.fctrUnknown
## 1
##
## Node number 1: 976 observations, complexity param=0.4878049
## predicted class=N expected loss=0.4620902 P(node) =1
## class counts: 525 451
## probabilities: 0.538 0.462
## left son=2 (536 obs) right son=3 (440 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=132.825300, (0 missing)
## startprice.log < 4.610145 to the right, improve=113.307100, (0 missing)
## idseq.my < 876.5 to the right, improve= 40.260140, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 12.703470, (0 missing)
## D.ratio.nstopwrds.nwrds < 0.269697 to the left, improve= 8.111339, (0 missing)
## Surrogate splits:
## startprice.log < 5.030417 to the right, agree=0.738, adj=0.418, (0 split)
## idseq.my < 869.5 to the right, agree=0.634, adj=0.189, (0 split)
## condition.fctrFor parts or not working < 0.5 to the left, agree=0.567, adj=0.039, (0 split)
## prdline.my.fctriPad 2 < 0.5 to the left, agree=0.565, adj=0.034, (0 split)
## D.npnct03.log < 0.3465736 to the left, agree=0.557, adj=0.018, (0 split)
##
## Node number 2: 536 observations
## predicted class=N expected loss=0.2257463 P(node) =0.5491803
## class counts: 415 121
## probabilities: 0.774 0.226
##
## Node number 3: 440 observations, complexity param=0.0443459
## predicted class=Y expected loss=0.25 P(node) =0.4508197
## class counts: 110 330
## probabilities: 0.250 0.750
## left son=6 (163 obs) right son=7 (277 obs)
## Primary splits:
## startprice.log < 4.923459 to the right, improve=38.162940, (0 missing)
## idseq.my < 920.5 to the right, improve=19.234430, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 3.803542, (0 missing)
## cellular.fctrUnknown < 0.5 to the right, improve= 3.795302, (0 missing)
## D.TfIdf.sum.post.stop < 7.237556 to the left, improve= 2.693422, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir < 0.5 to the right, agree=0.700, adj=0.190, (0 split)
## condition.fctrNew < 0.5 to the right, agree=0.684, adj=0.147, (0 split)
## color.fctrSpace Gray < 0.5 to the right, agree=0.675, adj=0.123, (0 split)
## prdline.my.fctriPadmini 2+ < 0.5 to the right, agree=0.666, adj=0.098, (0 split)
## condition.fctrNew other (see details) < 0.5 to the right, agree=0.652, adj=0.061, (0 split)
##
## Node number 6: 163 observations, complexity param=0.0443459
## predicted class=N expected loss=0.4785276 P(node) =0.1670082
## class counts: 85 78
## probabilities: 0.521 0.479
## left son=12 (68 obs) right son=13 (95 obs)
## Primary splits:
## idseq.my < 920.5 to the right, improve=17.345980, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve= 5.716666, (0 missing)
## startprice.log < 6.024113 to the right, improve= 5.432188, (0 missing)
## color.fctrSpace Gray < 0.5 to the left, improve= 4.308136, (0 missing)
## color.fctrBlack < 0.5 to the right, improve= 2.710165, (0 missing)
## Surrogate splits:
## storage.fctrUnknown < 0.5 to the right, agree=0.626, adj=0.103, (0 split)
## startprice.log < 6.024113 to the right, agree=0.626, adj=0.103, (0 split)
## condition.fctrFor parts or not working < 0.5 to the right, agree=0.620, adj=0.088, (0 split)
## D.ratio.sum.TfIdf.nwrds < 0.3691527 to the right, agree=0.613, adj=0.074, (0 split)
## D.T.great < 0.4655933 to the right, agree=0.613, adj=0.074, (0 split)
##
## Node number 7: 277 observations
## predicted class=Y expected loss=0.09025271 P(node) =0.2838115
## class counts: 25 252
## probabilities: 0.090 0.910
##
## Node number 12: 68 observations
## predicted class=N expected loss=0.2058824 P(node) =0.06967213
## class counts: 54 14
## probabilities: 0.794 0.206
##
## Node number 13: 95 observations
## predicted class=Y expected loss=0.3263158 P(node) =0.09733607
## class counts: 31 64
## probabilities: 0.326 0.674
##
## n= 976
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 976 451 N (0.53790984 0.46209016)
## 2) biddable< 0.5 536 121 N (0.77425373 0.22574627) *
## 3) biddable>=0.5 440 110 Y (0.25000000 0.75000000)
## 6) startprice.log>=4.923459 163 78 N (0.52147239 0.47852761)
## 12) idseq.my>=920.5 68 14 N (0.79411765 0.20588235) *
## 13) idseq.my< 920.5 95 31 Y (0.32631579 0.67368421) *
## 7) startprice.log< 4.923459 277 25 Y (0.09025271 0.90974729) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6320953
## 3 0.2 0.6320953
## 4 0.3 0.7679222
## 5 0.4 0.7679222
## 6 0.5 0.7679222
## 7 0.6 0.7679222
## 8 0.7 0.6923077
## 9 0.8 0.6923077
## 10 0.9 0.6923077
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 469
## 2 Y 135
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 56
## 2 316
## Prediction
## Reference N Y
## N 469 56
## Y 135 316
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.043033e-01 6.014231e-01 7.779982e-01 8.287575e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 5.945293e-68 1.662581e-08
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6321484
## 3 0.2 0.6321484
## 4 0.3 0.7742782
## 5 0.4 0.7742782
## 6 0.5 0.7742782
## 7 0.6 0.7742782
## 8 0.7 0.7102526
## 9 0.8 0.7102526
## 10 0.9 0.7102526
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 418
## 2 Y 114
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 58
## 2 295
## Prediction
## Reference N Y
## N 418 58
## Y 114 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.056497e-01 6.052550e-01 7.780145e-01 8.312324e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.814281e-62 2.744280e-05
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.429 0.055
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8116461 0.6 0.7679222 0.7981438
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7779982 0.8287575 0.5882503 0.8087593
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7742782 0.8056497
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7780145 0.8312324 0.605255
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01270241 0.02818363
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rpart 5 0 118.573 123.45 4.877
## 6 fit.models_1_rf 6 0 123.450 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 37 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 976 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1952 matrix numeric
## oob.times 976 -none- numeric
## classes 2 -none- character
## importance 72 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 976 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 72 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.7989371
## 3 0.2 0.9415449
## 4 0.3 0.9815016
## 5 0.4 0.9988926
## 6 0.5 1.0000000
## 7 0.6 0.9988901
## 8 0.7 0.9437939
## 9 0.8 0.8435897
## 10 0.9 0.7402235
## 11 1.0 0.1050420
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 525
## 2 Y NA
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 NA
## 2 451
## Prediction
## Reference N Y
## N 525 0
## Y 0 451
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.962275e-01 1.000000e+00 5.379098e-01
## AccuracyPValue McnemarPValue
## 1.487504e-263 NaN
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6627810
## 3 0.2 0.7034884
## 4 0.3 0.7519466
## 5 0.4 0.7776428
## 6 0.5 0.7864583
## 7 0.6 0.7715458
## 8 0.7 0.7460545
## 9 0.8 0.7187970
## 10 0.9 0.5939597
## 11 1.0 0.0477327
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 419
## 2 Y 107
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 57
## 2 302
## Prediction
## Reference N Y
## N 419 57
## Y 107 302
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.146893e-01 6.240072e-01 7.874927e-01 8.397715e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 5.523291e-67 1.301064e-04
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 12.717 3.881
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.7838068
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9962275 1 0.561723 0.8521579
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7864583 0.8146893
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7874927 0.8397715 0.6240072
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02492697 0.04716872# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#model_id <- "Rank9.2"; indep_vars_vctr <- c(NULL
# ,"<feat1>"
# ,"<feat1>*<feat2>"
# ,"<feat1>:<feat2>"
# )
# for (method in c("bayesglm")) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
# }
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.log
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.log
## Max.cor.Y.rpart biddable, startprice.log
## Max.cor.Y.glm biddable, startprice.log
## Interact.High.cor.Y.glm biddable, startprice.log, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.glmnet biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.323
## Random.myrandom_classfr 0 0.259
## Max.cor.Y.cv.0.rpart 0 0.624
## Max.cor.Y.cv.0.cp.0.rpart 0 0.464
## Max.cor.Y.rpart 3 1.013
## Max.cor.Y.glm 1 0.940
## Interact.High.cor.Y.glm 1 0.963
## Low.cor.X.glm 1 1.176
## All.X.glm 1 1.311
## All.X.bayesglm 1 2.156
## All.X.glmnet 9 6.821
## All.X.no.rnorm.rpart 3 1.429
## All.X.no.rnorm.rf 3 12.717
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.003 0.5000000
## Random.myrandom_classfr 0.002 0.5143786
## Max.cor.Y.cv.0.rpart 0.011 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.008 0.8754662
## Max.cor.Y.rpart 0.012 0.8085735
## Max.cor.Y.glm 0.011 0.8349826
## Interact.High.cor.Y.glm 0.015 0.8396790
## Low.cor.X.glm 0.104 0.8658642
## All.X.glm 0.175 0.8761525
## All.X.bayesglm 0.298 0.8725837
## All.X.glmnet 1.940 0.8401225
## All.X.no.rnorm.rpart 0.055 0.8116461
## All.X.no.rnorm.rf 3.881 1.0000000
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320953
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.8030303
## Max.cor.Y.rpart 0.5 0.7517241
## Max.cor.Y.glm 0.5 0.7471526
## Interact.High.cor.Y.glm 0.5 0.7465116
## Low.cor.X.glm 0.5 0.7808858
## All.X.glm 0.5 0.7847222
## All.X.bayesglm 0.5 0.7819026
## All.X.glmnet 0.5 0.7435610
## All.X.no.rnorm.rpart 0.6 0.7679222
## All.X.no.rnorm.rf 0.5 1.0000000
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.5379098 0.5060449
## Random.myrandom_classfr 0.4620902 0.4304545
## Max.cor.Y.cv.0.rpart 0.5379098 0.5060449
## Max.cor.Y.cv.0.cp.0.rpart 0.8135246 0.7876443
## Max.cor.Y.rpart 0.7745729 0.7513118
## Max.cor.Y.glm 0.7694604 0.7449290
## Interact.High.cor.Y.glm 0.7704987 0.7491833
## Low.cor.X.glm 0.7602360 0.7812112
## All.X.glm 0.7633192 0.7833545
## All.X.bayesglm 0.7684379 0.7812112
## All.X.glmnet 0.7694730 0.7374925
## All.X.no.rnorm.rpart 0.7981438 0.7779982
## All.X.no.rnorm.rf 0.7838068 0.9962275
## max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5695455 0.0000000 0.5000000
## Random.myrandom_classfr 0.4939551 0.0000000 0.5214656
## Max.cor.Y.cv.0.rpart 0.5695455 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8374971 0.6261780 0.8186805
## Max.cor.Y.rpart 0.8043723 0.5393250 0.8157399
## Max.cor.Y.glm 0.7984977 0.5343223 0.8432177
## Interact.High.cor.Y.glm 0.8024150 0.5354264 0.8384587
## Low.cor.X.glm 0.8316731 0.5157767 0.8449025
## All.X.glm 0.8336156 0.5226601 0.8391188
## All.X.bayesglm 0.8316731 0.5326115 0.8410552
## All.X.glmnet 0.7916339 0.5352804 0.8482361
## All.X.no.rnorm.rpart 0.8287575 0.5882503 0.8087593
## All.X.no.rnorm.rf 1.0000000 0.5617230 0.8521579
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6321484
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6 0.7634961
## Max.cor.Y.rpart 0.5 0.7705957
## Max.cor.Y.glm 0.4 0.7701711
## Interact.High.cor.Y.glm 0.4 0.7699877
## Low.cor.X.glm 0.5 0.7623888
## All.X.glm 0.5 0.7575000
## All.X.bayesglm 0.5 0.7614213
## All.X.glmnet 0.5 0.7721046
## All.X.no.rnorm.rpart 0.6 0.7742782
## All.X.no.rnorm.rf 0.5 0.7864583
## max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.5378531 0.5043611
## Random.myrandom_classfr 0.4621469 0.4289077
## Max.cor.Y.cv.0.rpart 0.5378531 0.5043611
## Max.cor.Y.cv.0.cp.0.rpart 0.7920904 0.7638373
## Max.cor.Y.rpart 0.7954802 0.7673772
## Max.cor.Y.glm 0.7875706 0.7591217
## Interact.High.cor.Y.glm 0.7887006 0.7603001
## Low.cor.X.glm 0.7887006 0.7603001
## All.X.glm 0.7807910 0.7520575
## All.X.bayesglm 0.7875706 0.7591217
## All.X.glmnet 0.7932203 0.7650169
## All.X.no.rnorm.rpart 0.8056497 0.7780145
## All.X.no.rnorm.rf 0.8146893 0.7874927
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5710923 0.0000000
## Random.myrandom_classfr 0.4956389 0.0000000
## Max.cor.Y.cv.0.rpart 0.5710923 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8183833 0.5788853
## Max.cor.Y.rpart 0.8215999 0.5865393
## Max.cor.Y.glm 0.8140900 0.5726922
## Interact.High.cor.Y.glm 0.8151638 0.5745991
## Low.cor.X.glm 0.8151638 0.5726857
## All.X.glm 0.8076410 0.5576847
## All.X.bayesglm 0.8140900 0.5704749
## All.X.glmnet 0.8194558 0.5829804
## All.X.no.rnorm.rpart 0.8312324 0.6052550
## All.X.no.rnorm.rf 0.8397715 0.6240072
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA NA
## Random.myrandom_classfr NA NA NA
## Max.cor.Y.cv.0.rpart NA NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA NA
## Max.cor.Y.rpart 0.0235259516 0.053611532 NA
## Max.cor.Y.glm 0.0065013915 0.011856863 962.8176
## Interact.High.cor.Y.glm 0.0059986624 0.012913819 961.8295
## Low.cor.X.glm 0.0136595379 0.027549590 980.6074
## All.X.glm 0.0004195939 0.001563689 985.1018
## All.X.bayesglm 0.0073158123 0.015033572 1000.2163
## All.X.glmnet 0.0078724769 0.017950753 NA
## All.X.no.rnorm.rpart 0.0127024133 0.028183629 NA
## All.X.no.rnorm.rf 0.0249269655 0.047168722 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rf 6 0 123.45 139.69 16.24
## 7 fit.models_1_end 7 0 139.69 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 93.262 139.697 46.435
## 12 fit.models 7 2 139.697 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.log
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.log
## Max.cor.Y.rpart biddable, startprice.log
## Max.cor.Y.glm biddable, startprice.log
## Interact.High.cor.Y.glm biddable, startprice.log, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.glmnet biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, .rnorm, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5143786
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.8754662
## Max.cor.Y.rpart 3 0.8085735
## Max.cor.Y.glm 1 0.8349826
## Interact.High.cor.Y.glm 1 0.8396790
## Low.cor.X.glm 1 0.8658642
## All.X.glm 1 0.8761525
## All.X.bayesglm 1 0.8725837
## All.X.glmnet 9 0.8401225
## All.X.no.rnorm.rpart 3 0.8116461
## All.X.no.rnorm.rf 3 1.0000000
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320953
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.8030303
## Max.cor.Y.rpart 0.5 0.7517241
## Max.cor.Y.glm 0.5 0.7471526
## Interact.High.cor.Y.glm 0.5 0.7465116
## Low.cor.X.glm 0.5 0.7808858
## All.X.glm 0.5 0.7847222
## All.X.bayesglm 0.5 0.7819026
## All.X.glmnet 0.5 0.7435610
## All.X.no.rnorm.rpart 0.6 0.7679222
## All.X.no.rnorm.rf 0.5 1.0000000
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5379098 0.0000000 0.5000000
## Random.myrandom_classfr 0.4620902 0.0000000 0.5214656
## Max.cor.Y.cv.0.rpart 0.5379098 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8135246 0.6261780 0.8186805
## Max.cor.Y.rpart 0.7745729 0.5393250 0.8157399
## Max.cor.Y.glm 0.7694604 0.5343223 0.8432177
## Interact.High.cor.Y.glm 0.7704987 0.5354264 0.8384587
## Low.cor.X.glm 0.7602360 0.5157767 0.8449025
## All.X.glm 0.7633192 0.5226601 0.8391188
## All.X.bayesglm 0.7684379 0.5326115 0.8410552
## All.X.glmnet 0.7694730 0.5352804 0.8482361
## All.X.no.rnorm.rpart 0.7981438 0.5882503 0.8087593
## All.X.no.rnorm.rf 0.7838068 0.5617230 0.8521579
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6321484
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6 0.7634961
## Max.cor.Y.rpart 0.5 0.7705957
## Max.cor.Y.glm 0.4 0.7701711
## Interact.High.cor.Y.glm 0.4 0.7699877
## Low.cor.X.glm 0.5 0.7623888
## All.X.glm 0.5 0.7575000
## All.X.bayesglm 0.5 0.7614213
## All.X.glmnet 0.5 0.7721046
## All.X.no.rnorm.rpart 0.6 0.7742782
## All.X.no.rnorm.rf 0.5 0.7864583
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5378531 0.0000000
## Random.myrandom_classfr 0.4621469 0.0000000
## Max.cor.Y.cv.0.rpart 0.5378531 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7920904 0.5788853
## Max.cor.Y.rpart 0.7954802 0.5865393
## Max.cor.Y.glm 0.7875706 0.5726922
## Interact.High.cor.Y.glm 0.7887006 0.5745991
## Low.cor.X.glm 0.7887006 0.5726857
## All.X.glm 0.7807910 0.5576847
## All.X.bayesglm 0.7875706 0.5704749
## All.X.glmnet 0.7932203 0.5829804
## All.X.no.rnorm.rpart 0.8056497 0.6052550
## All.X.no.rnorm.rf 0.8146893 0.6240072
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.myMFO_classfr 3.0959752 333.3333333
## Random.myrandom_classfr 3.8610039 500.0000000
## Max.cor.Y.cv.0.rpart 1.6025641 90.9090909
## Max.cor.Y.cv.0.cp.0.rpart 2.1551724 125.0000000
## Max.cor.Y.rpart 0.9871668 83.3333333
## Max.cor.Y.glm 1.0638298 90.9090909
## Interact.High.cor.Y.glm 1.0384216 66.6666667
## Low.cor.X.glm 0.8503401 9.6153846
## All.X.glm 0.7627765 5.7142857
## All.X.bayesglm 0.4638219 3.3557047
## All.X.glmnet 0.1466061 0.5154639
## All.X.no.rnorm.rpart 0.6997901 18.1818182
## All.X.no.rnorm.rf 0.0786349 0.2576656
## inv.aic.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.glm 0.0010386183
## Interact.High.cor.Y.glm 0.0010396853
## Low.cor.X.glm 0.0010197761
## All.X.glm 0.0010151235
## All.X.bayesglm 0.0009997838
## All.X.glmnet NA
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually if you must have them.## Warning: Removed 5 rows containing missing values (geom_path).## Warning: Removed 103 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 13 All.X.no.rnorm.rf 0.8146893 0.8521579 0.6240072
## 12 All.X.no.rnorm.rpart 0.8056497 0.8087593 0.6052550
## 5 Max.cor.Y.rpart 0.7954802 0.8157399 0.5865393
## 11 All.X.glmnet 0.7932203 0.8482361 0.5829804
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7920904 0.8186805 0.5788853
## 8 Low.cor.X.glm 0.7887006 0.8449025 0.5726857
## 7 Interact.High.cor.Y.glm 0.7887006 0.8384587 0.5745991
## 6 Max.cor.Y.glm 0.7875706 0.8432177 0.5726922
## 10 All.X.bayesglm 0.7875706 0.8410552 0.5704749
## 9 All.X.glm 0.7807910 0.8391188 0.5576847
## 1 MFO.myMFO_classfr 0.5378531 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5378531 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.4621469 0.5214656 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 13 NA 0.5
## 12 NA 0.6
## 5 NA 0.5
## 11 NA 0.5
## 4 NA 0.6
## 8 980.6074 0.5
## 7 961.8295 0.4
## 6 962.8176 0.4
## 10 1000.2163 0.5
## 9 985.1018 0.5
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually if you must have them.## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.X.no.rnorm.rf"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 976 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1952 matrix numeric
## oob.times 976 -none- numeric
## classes 2 -none- character
## importance 72 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 976 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 72 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## startprice.log 100.00000000
## biddable 75.77557376
## idseq.my 61.89274395
## D.ratio.sum.TfIdf.nwrds 6.55080925
## D.TfIdf.sum.stem.stop.Ratio 5.46221930
## color.fctrBlack 5.42672497
## storage.fctr64 4.54591071
## D.ratio.nstopwrds.nwrds 4.48486137
## prdline.my.fctriPadAir 4.21882380
## color.fctrWhite 4.14701866
## prdline.my.fctriPad 1 4.06373906
## D.sum.TfIdf 3.99443151
## condition.fctrNew 3.99149067
## D.TfIdf.sum.post.stem 3.95656358
## storage.fctr16 3.93700840
## color.fctrSpace Gray 3.85738741
## D.TfIdf.sum.post.stop 3.84367131
## cellular.fctr1 3.74587984
## D.nuppr.log 3.68920021
## D.nchrs.log 3.42398279
## prdline.my.fctriPadmini 3.32197629
## prdline.my.fctriPad 3+ 3.25104872
## carrier.fctrAT&T 3.11118805
## cellular.fctrUnknown 3.00719785
## carrier.fctrUnknown 2.90117620
## storage.fctrUnknown 2.86941294
## D.nstopwrds.log 2.72664697
## carrier.fctrVerizon 2.61940946
## D.nwrds.log 2.60132737
## storage.fctr32 2.55032142
## condition.fctrNew other (see details) 2.45350758
## prdline.my.fctriPad 2 2.39999031
## D.T.use 2.25809141
## D.T.condit 2.12215929
## condition.fctrSeller refurbished 2.10269385
## D.nwrds.unq.log 2.04380750
## D.npnct11.log 1.99781559
## D.T.scratch 1.98124045
## prdline.my.fctriPadmini 2+ 1.96047393
## D.terms.n.post.stem 1.82074780
## D.terms.n.post.stop 1.76498655
## D.T.good 1.68403568
## D.T.new 1.65458838
## D.T.screen 1.61414375
## D.npnct13.log 1.54450514
## D.T.ipad 1.51703617
## condition.fctrManufacturer refurbished 1.33277394
## condition.fctrFor parts or not working 1.31102748
## color.fctrGold 1.27778210
## D.T.work 1.23378640
## carrier.fctrSprint 1.01108476
## D.T.great 0.95409170
## D.npnct15.log 0.90427738
## D.ndgts.log 0.81399761
## D.terms.n.stem.stop.Ratio 0.79621742
## D.T.excel 0.78865866
## carrier.fctrT-Mobile 0.66059489
## D.npnct14.log 0.54157235
## D.npnct05.log 0.50191492
## D.npnct01.log 0.44250690
## D.npnct08.log 0.43283592
## D.npnct24.log 0.42904030
## D.npnct12.log 0.40417016
## D.npnct10.log 0.39734986
## D.npnct16.log 0.27323339
## carrier.fctrOther 0.16984240
## D.npnct03.log 0.11963549
## D.P.mini 0.10241921
## D.npnct06.log 0.09382797
## D.P.air 0.06502214
## D.npnct09.log 0.00357743
## D.npnct28.log 0.00000000
## All.X.no.rnorm.rf.importance
## startprice.log 100.00000000
## biddable 75.77557376
## idseq.my 61.89274395
## D.ratio.sum.TfIdf.nwrds 6.55080925
## D.TfIdf.sum.stem.stop.Ratio 5.46221930
## color.fctrBlack 5.42672497
## storage.fctr64 4.54591071
## D.ratio.nstopwrds.nwrds 4.48486137
## prdline.my.fctriPadAir 4.21882380
## color.fctrWhite 4.14701866
## prdline.my.fctriPad 1 4.06373906
## D.sum.TfIdf 3.99443151
## condition.fctrNew 3.99149067
## D.TfIdf.sum.post.stem 3.95656358
## storage.fctr16 3.93700840
## color.fctrSpace Gray 3.85738741
## D.TfIdf.sum.post.stop 3.84367131
## cellular.fctr1 3.74587984
## D.nuppr.log 3.68920021
## D.nchrs.log 3.42398279
## prdline.my.fctriPadmini 3.32197629
## prdline.my.fctriPad 3+ 3.25104872
## carrier.fctrAT&T 3.11118805
## cellular.fctrUnknown 3.00719785
## carrier.fctrUnknown 2.90117620
## storage.fctrUnknown 2.86941294
## D.nstopwrds.log 2.72664697
## carrier.fctrVerizon 2.61940946
## D.nwrds.log 2.60132737
## storage.fctr32 2.55032142
## condition.fctrNew other (see details) 2.45350758
## prdline.my.fctriPad 2 2.39999031
## D.T.use 2.25809141
## D.T.condit 2.12215929
## condition.fctrSeller refurbished 2.10269385
## D.nwrds.unq.log 2.04380750
## D.npnct11.log 1.99781559
## D.T.scratch 1.98124045
## prdline.my.fctriPadmini 2+ 1.96047393
## D.terms.n.post.stem 1.82074780
## D.terms.n.post.stop 1.76498655
## D.T.good 1.68403568
## D.T.new 1.65458838
## D.T.screen 1.61414375
## D.npnct13.log 1.54450514
## D.T.ipad 1.51703617
## condition.fctrManufacturer refurbished 1.33277394
## condition.fctrFor parts or not working 1.31102748
## color.fctrGold 1.27778210
## D.T.work 1.23378640
## carrier.fctrSprint 1.01108476
## D.T.great 0.95409170
## D.npnct15.log 0.90427738
## D.ndgts.log 0.81399761
## D.terms.n.stem.stop.Ratio 0.79621742
## D.T.excel 0.78865866
## carrier.fctrT-Mobile 0.66059489
## D.npnct14.log 0.54157235
## D.npnct05.log 0.50191492
## D.npnct01.log 0.44250690
## D.npnct08.log 0.43283592
## D.npnct24.log 0.42904030
## D.npnct12.log 0.40417016
## D.npnct10.log 0.39734986
## D.npnct16.log 0.27323339
## carrier.fctrOther 0.16984240
## D.npnct03.log 0.11963549
## D.P.mini 0.10241921
## D.npnct06.log 0.09382797
## D.P.air 0.06502214
## D.npnct09.log 0.00357743
## D.npnct28.log 0.00000000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 51
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 66 10066 N 0.166
## 951 10951 N 0.036
## 285 10285 Y 0.984
## 1517 11517 N 0.512
## 1502 11502 N 0.516
## 1309 11309 N 0.536
## 851 10851 N 0.544
## 602 10602 N 0.552
## 512 10512 N 0.600
## 1836 11836 N 0.626
## 1526 11526 N 0.648
## 127 10127 N 0.654
## 1700 11700 N 0.836
## 1769 11769 N 0.878
## 199 10199 N 0.990
## sold.fctr.predict.All.X.no.rnorm.rf
## 66 N
## 951 N
## 285 Y
## 1517 Y
## 1502 Y
## 1309 Y
## 851 Y
## 602 Y
## 512 Y
## 1836 Y
## 1526 Y
## 127 Y
## 1700 Y
## 1769 Y
## 199 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 66 TRUE
## 951 TRUE
## 285 TRUE
## 1517 FALSE
## 1502 FALSE
## 1309 FALSE
## 851 FALSE
## 602 FALSE
## 512 FALSE
## 1836 FALSE
## 1526 FALSE
## 127 FALSE
## 1700 FALSE
## 1769 FALSE
## 199 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error .label
## 66 0.000 10066
## 951 0.000 10951
## 285 0.000 10285
## 1517 0.012 11517
## 1502 0.016 11502
## 1309 0.036 11309
## 851 0.044 10851
## 602 0.052 10602
## 512 0.100 10512
## 1836 0.126 11836
## 1526 0.148 11526
## 127 0.154 10127
## 1700 0.336 11700
## 1769 0.378 11769
## 199 0.490 10199
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 1697 11697 Y 0.012
## 1359 11359 Y 0.024
## 962 10962 Y 0.028
## 1605 11605 Y 0.034
## 1212 11212 Y 0.040
## 846 10846 Y 0.040
## sold.fctr.predict.All.X.no.rnorm.rf
## 1697 N
## 1359 N
## 962 N
## 1605 N
## 1212 N
## 846 N
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 1697 FALSE
## 1359 FALSE
## 962 FALSE
## 1605 FALSE
## 1212 FALSE
## 846 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 1697 -0.488
## 1359 -0.476
## 962 -0.472
## 1605 -0.466
## 1212 -0.460
## 846 -0.460
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 693 10693 Y 0.260
## 369 10369 N 0.692
## 120 10120 N 0.776
## 529 10529 N 0.790
## 569 10569 N 0.842
## 488 10488 N 0.984
## sold.fctr.predict.All.X.no.rnorm.rf
## 693 N
## 369 Y
## 120 Y
## 529 Y
## 569 Y
## 488 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 693 FALSE
## 369 FALSE
## 120 FALSE
## 529 FALSE
## 569 FALSE
## 488 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 693 -0.240
## 369 0.192
## 120 0.276
## 529 0.290
## 569 0.342
## 488 0.484
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 413 10413 N 0.964
## 526 10526 N 0.978
## 491 10491 N 0.978
## 283 10283 N 0.982
## 488 10488 N 0.984
## 199 10199 N 0.990
## sold.fctr.predict.All.X.no.rnorm.rf
## 413 Y
## 526 Y
## 491 Y
## 283 Y
## 488 Y
## 199 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 413 FALSE
## 526 FALSE
## 491 FALSE
## 283 FALSE
## 488 FALSE
## 199 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 413 0.464
## 526 0.478
## 491 0.478
## 283 0.482
## 488 0.484
## 199 0.490
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 139.697 155.048 15.351
## 13 fit.models 7 3 155.049 NA NA if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.models_3: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.All.X.no.rnorm.rf.prob"
## [2] "sold.fctr.predict.All.X.no.rnorm.rf"
## [3] "sold.fctr.predict.All.X.no.rnorm.rf.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 155.049 159.191 4.142
## 14 fit.data.training 8 0 159.191 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.data.training_0: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:randomForest':
##
## combine
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log"
## Aggregating results
## Fitting final model on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 1861 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 3722 matrix numeric
## oob.times 1861 -none- numeric
## classes 2 -none- character
## importance 72 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 1861 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 72 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6321205
## 2 0.1 0.8140085
## 3 0.2 0.9398907
## 4 0.3 0.9817352
## 5 0.4 0.9982589
## 6 0.5 1.0000000
## 7 0.6 0.9994183
## 8 0.7 0.9408867
## 9 0.8 0.8548602
## 10 0.9 0.7634795
## 11 1.0 0.1015453## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Final.rf.N sold.fctr.predict.Final.rf.Y
## 1 N 1001 NA
## 2 Y NA 860
## Prediction
## Reference N Y
## N 1001 0
## Y 0 860
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.0000000 1.0000000 0.9980198 1.0000000 0.5378829
## AccuracyPValue McnemarPValue
## 0.0000000 NaN## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.excel, D.npnct03.log, D.T.screen, D.terms.n.stem.stop.Ratio, D.T.use, D.ratio.sum.TfIdf.nwrds, D.T.great, D.npnct01.log, D.T.good, D.TfIdf.sum.stem.stop.Ratio, D.T.scratch, D.P.air, storage.fctr, D.P.mini, D.T.work, D.T.ipad, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.T.condit, D.T.new, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 17.041 9.403
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.8119301
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9980198 1 0.6177489
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008238765 0.01670118rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 159.191 179.61 20.419
## 15 fit.data.training 8 1 179.611 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.5sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## All.X.no.rnorm.rf.importance
## startprice.log 100.00000000
## biddable 75.77557376
## idseq.my 61.89274395
## D.ratio.sum.TfIdf.nwrds 6.55080925
## D.TfIdf.sum.stem.stop.Ratio 5.46221930
## prdline.my.fctriPadAir 4.21882380
## prdline.my.fctriPad 1 4.06373906
## D.ratio.nstopwrds.nwrds 4.48486137
## color.fctrWhite 4.14701866
## D.TfIdf.sum.post.stop 3.84367131
## cellular.fctr1 3.74587984
## color.fctrBlack 5.42672497
## prdline.my.fctriPad 3+ 3.25104872
## storage.fctr64 4.54591071
## D.TfIdf.sum.post.stem 3.95656358
## D.nchrs.log 3.42398279
## carrier.fctrUnknown 2.90117620
## storage.fctr16 3.93700840
## D.sum.TfIdf 3.99443151
## condition.fctrNew 3.99149067
## prdline.my.fctriPadmini 3.32197629
## D.nstopwrds.log 2.72664697
## D.nuppr.log 3.68920021
## prdline.my.fctriPad 2 2.39999031
## color.fctrSpace Gray 3.85738741
## prdline.my.fctriPadmini 2+ 1.96047393
## D.nwrds.log 2.60132737
## storage.fctr32 2.55032142
## storage.fctrUnknown 2.86941294
## cellular.fctrUnknown 3.00719785
## carrier.fctrVerizon 2.61940946
## carrier.fctrAT&T 3.11118805
## D.T.use 2.25809141
## condition.fctrNew other (see details) 2.45350758
## D.T.condit 2.12215929
## condition.fctrSeller refurbished 2.10269385
## D.terms.n.post.stem 1.82074780
## D.terms.n.post.stop 1.76498655
## D.nwrds.unq.log 2.04380750
## D.T.scratch 1.98124045
## D.npnct11.log 1.99781559
## condition.fctrFor parts or not working 1.31102748
## D.T.good 1.68403568
## condition.fctrManufacturer refurbished 1.33277394
## D.T.excel 0.78865866
## D.npnct13.log 1.54450514
## D.T.great 0.95409170
## color.fctrGold 1.27778210
## D.T.new 1.65458838
## D.T.ipad 1.51703617
## D.T.screen 1.61414375
## D.T.work 1.23378640
## D.ndgts.log 0.81399761
## D.npnct15.log 0.90427738
## carrier.fctrSprint 1.01108476
## carrier.fctrT-Mobile 0.66059489
## D.npnct16.log 0.27323339
## D.terms.n.stem.stop.Ratio 0.79621742
## D.npnct05.log 0.50191492
## D.npnct08.log 0.43283592
## D.npnct14.log 0.54157235
## D.npnct24.log 0.42904030
## carrier.fctrOther 0.16984240
## D.npnct01.log 0.44250690
## D.npnct06.log 0.09382797
## D.npnct12.log 0.40417016
## D.P.mini 0.10241921
## D.npnct10.log 0.39734986
## D.npnct03.log 0.11963549
## D.P.air 0.06502214
## D.npnct28.log 0.00000000
## D.npnct09.log 0.00357743
## importance Final.rf.importance
## startprice.log 1.000000e+02 1.000000e+02
## biddable 9.822028e+01 9.822028e+01
## idseq.my 6.664811e+01 6.664811e+01
## D.ratio.sum.TfIdf.nwrds 5.501794e+00 5.501794e+00
## D.TfIdf.sum.stem.stop.Ratio 5.470859e+00 5.470859e+00
## prdline.my.fctriPadAir 5.122997e+00 5.122997e+00
## prdline.my.fctriPad 1 5.076772e+00 5.076772e+00
## D.ratio.nstopwrds.nwrds 4.620204e+00 4.620204e+00
## color.fctrWhite 4.544942e+00 4.544942e+00
## D.TfIdf.sum.post.stop 4.540872e+00 4.540872e+00
## cellular.fctr1 4.301726e+00 4.301726e+00
## color.fctrBlack 4.027819e+00 4.027819e+00
## prdline.my.fctriPad 3+ 3.895146e+00 3.895146e+00
## storage.fctr64 3.854447e+00 3.854447e+00
## D.TfIdf.sum.post.stem 3.853459e+00 3.853459e+00
## D.nchrs.log 3.818113e+00 3.818113e+00
## carrier.fctrUnknown 3.815033e+00 3.815033e+00
## storage.fctr16 3.751024e+00 3.751024e+00
## D.sum.TfIdf 3.668908e+00 3.668908e+00
## condition.fctrNew 3.633870e+00 3.633870e+00
## prdline.my.fctriPadmini 3.506140e+00 3.506140e+00
## D.nstopwrds.log 3.451131e+00 3.451131e+00
## D.nuppr.log 3.439784e+00 3.439784e+00
## prdline.my.fctriPad 2 3.421780e+00 3.421780e+00
## color.fctrSpace Gray 3.280611e+00 3.280611e+00
## prdline.my.fctriPadmini 2+ 2.880925e+00 2.880925e+00
## D.nwrds.log 2.868460e+00 2.868460e+00
## storage.fctr32 2.837569e+00 2.837569e+00
## storage.fctrUnknown 2.610476e+00 2.610476e+00
## cellular.fctrUnknown 2.601186e+00 2.601186e+00
## carrier.fctrVerizon 2.322727e+00 2.322727e+00
## carrier.fctrAT&T 2.242393e+00 2.242393e+00
## D.T.use 2.239005e+00 2.239005e+00
## condition.fctrNew other (see details) 2.235349e+00 2.235349e+00
## D.T.condit 2.226344e+00 2.226344e+00
## condition.fctrSeller refurbished 2.203554e+00 2.203554e+00
## D.terms.n.post.stem 2.160570e+00 2.160570e+00
## D.terms.n.post.stop 2.005976e+00 2.005976e+00
## D.nwrds.unq.log 1.943754e+00 1.943754e+00
## D.T.scratch 1.917683e+00 1.917683e+00
## D.npnct11.log 1.833161e+00 1.833161e+00
## condition.fctrFor parts or not working 1.597595e+00 1.597595e+00
## D.T.good 1.583586e+00 1.583586e+00
## condition.fctrManufacturer refurbished 1.496177e+00 1.496177e+00
## D.T.excel 1.447960e+00 1.447960e+00
## D.npnct13.log 1.402395e+00 1.402395e+00
## D.T.great 1.393428e+00 1.393428e+00
## color.fctrGold 1.376307e+00 1.376307e+00
## D.T.new 1.367479e+00 1.367479e+00
## D.T.ipad 1.350443e+00 1.350443e+00
## D.T.screen 1.328451e+00 1.328451e+00
## D.T.work 1.246854e+00 1.246854e+00
## D.ndgts.log 1.116139e+00 1.116139e+00
## D.npnct15.log 1.095564e+00 1.095564e+00
## carrier.fctrSprint 1.046308e+00 1.046308e+00
## carrier.fctrT-Mobile 8.766188e-01 8.766188e-01
## D.npnct16.log 5.862696e-01 5.862696e-01
## D.terms.n.stem.stop.Ratio 5.595316e-01 5.595316e-01
## D.npnct05.log 5.211130e-01 5.211130e-01
## D.npnct08.log 4.850993e-01 4.850993e-01
## D.npnct14.log 3.647107e-01 3.647107e-01
## D.npnct24.log 3.386245e-01 3.386245e-01
## carrier.fctrOther 3.349017e-01 3.349017e-01
## D.npnct01.log 3.115916e-01 3.115916e-01
## D.npnct06.log 3.042029e-01 3.042029e-01
## D.npnct12.log 3.027261e-01 3.027261e-01
## D.P.mini 2.438581e-01 2.438581e-01
## D.npnct10.log 2.409854e-01 2.409854e-01
## D.npnct03.log 1.677621e-01 1.677621e-01
## D.P.air 4.503618e-02 4.503618e-02
## D.npnct28.log 1.611318e-03 1.611318e-03
## D.npnct09.log 0.000000e+00 0.000000e+00if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 51
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1 10001 N 0.228
## 2 10002 Y 1.000
## 91 10091 Y 1.000
## 1397 11397 N 0.010
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1 N TRUE
## 2 Y TRUE
## 91 Y TRUE
## 1397 N TRUE
## sold.fctr.predict.Final.rf.error .label
## 1 0 10001
## 2 0 10002
## 91 0 10091
## 1397 0 11397
## [1] "Inaccurate: "
## [1] UniqueID sold.fctr
## [3] sold.fctr.predict.Final.rf.prob sold.fctr.predict.Final.rf
## [5] sold.fctr.predict.Final.rf.accurate sold.fctr.predict.Final.rf.error
## <0 rows> (or 0-length row.names)
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.Final.rf.prob" "sold.fctr.predict.Final.rf"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 179.611 184.818 5.207
## 16 predict.data.new 9 0 184.819 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.5if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 51## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1862 11862 <NA> 0.280
## 1865 11865 <NA> 0.500
## 1891 11891 <NA> 0.858
## 2625 12625 <NA> 0.380
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1862 N NA
## 1865 Y NA
## 1891 Y NA
## 2625 N NA
## sold.fctr.predict.Final.rf.error .label
## 1862 0 11862
## 1865 0 11865
## 1891 0 11891
## 2625 0 12625
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA <NA> NA
## NA.1 <NA> NA
## NA.2 <NA> NA
## NA.3 <NA> NA
## NA.4 <NA> NA
## NA.5 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA.12 NA <NA> NA
## NA.319 NA <NA> NA
## NA.341 NA <NA> NA
## NA.352 NA <NA> NA
## NA.615 NA <NA> NA
## NA.618 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA.12 <NA> NA
## NA.319 <NA> NA
## NA.341 <NA> NA
## NA.352 <NA> NA
## NA.615 <NA> NA
## NA.618 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA.12 NA
## NA.319 NA
## NA.341 NA
## NA.352 NA
## NA.615 NA
## NA.618 NA
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA.792 NA <NA> NA
## NA.793 NA <NA> NA
## NA.794 NA <NA> NA
## NA.795 NA <NA> NA
## NA.796 NA <NA> NA
## NA.797 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA.792 <NA> NA
## NA.793 <NA> NA
## NA.794 <NA> NA
## NA.795 <NA> NA
## NA.796 <NA> NA
## NA.797 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA.792 NA
## NA.793 NA
## NA.794 NA
## NA.795 NA
## NA.796 NA
## NA.797 NA## Warning: Removed 798 rows containing missing values (geom_point).
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
print(sprintf(" All post-stop-words TfIDf terms for %s:", txt_var))
myprint_df(glb_post_stop_words_terms_df_lst[[txt_var]])
TfIdf_mtrx <- glb_post_stop_words_TfIdf_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(TfIdf_mtrx[, tail(glb_post_stop_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stop_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(TfIdf_mtrx[, 246] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#TfIdf_mtrx[2442, TfIdf_mtrx[2442, ] > 0]
print(sprintf(" Top_n post-stop-words TfIDf terms for %s:", txt_var))
tmp_df <- glb_post_stop_words_terms_df_lst[[txt_var]]
top_n_vctr <- tmp_df$term[1:glb_top_n[[txt_var]]]
tmp_freq1_df <- subset(tmp_df, freq == 1)
tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stop-words TfIDf terms for descr.my:"
## TfIdf term freq pos
## condition 207.6392 condition 497 163
## new 125.1790 new 156 484
## used 122.6714 used 237 781
## good 120.6369 good 197 321
## scratches 113.4465 scratches 254 641
## screen 105.8086 screen 210 643
## TfIdf term freq pos
## air 14.780926 air 21 42
## little 12.739438 little 13 421
## properly 5.217255 properly 5 578
## auction 3.769610 auction 4 67
## card 2.485587 card 3 126
## consumer 1.137667 consumer 1 171
## TfIdf term freq pos
## said 1.1376668 said 1 634
## sales 1.1376668 sales 1 636
## seconds 1.1376668 seconds 1 651
## setup 1.1376668 setup 1 661
## shipment 1.1376668 shipment 1 668
## 79in 0.9480557 79in 1 16
## UniqueID
## 520 10520
## descr.my
## 520 Apple iPad mini 1st Generation 16GB, Wi- Fi, 7.9in - Space Gray, great condition comes with the
## [1] 268
## [1] " Top_n post-stop-words TfIDf terms for descr.my:"
## [1] TfIdf term freq pos top_n
## <0 rows> (or 0-length row.names)if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.5print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.X.no.rnorm.rf"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.rf"print(dim(glb_fitobs_df))## [1] 976 66print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 13 All.X.no.rnorm.rf 0.8146893 0.8521579 0.6240072
## 12 All.X.no.rnorm.rpart 0.8056497 0.8087593 0.6052550
## 5 Max.cor.Y.rpart 0.7954802 0.8157399 0.5865393
## 11 All.X.glmnet 0.7932203 0.8482361 0.5829804
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7920904 0.8186805 0.5788853
## 8 Low.cor.X.glm 0.7887006 0.8449025 0.5726857
## 7 Interact.High.cor.Y.glm 0.7887006 0.8384587 0.5745991
## 6 Max.cor.Y.glm 0.7875706 0.8432177 0.5726922
## 10 All.X.bayesglm 0.7875706 0.8410552 0.5704749
## 9 All.X.glm 0.7807910 0.8391188 0.5576847
## 1 MFO.myMFO_classfr 0.5378531 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5378531 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.4621469 0.5214656 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 13 NA 0.5
## 12 NA 0.6
## 5 NA 0.5
## 11 NA 0.5
## 4 NA 0.6
## 8 980.6074 0.5
## 7 961.8295 0.4
## 6 962.8176 0.4
## 10 1000.2163 0.5
## 9 985.1018 0.5
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_vars)) {
stop("not implemented yet")
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_vars)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "All.X.no.rnorm.rf OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 419 57
## Y 107 302
## prdline.my .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 171 154 0.1929825 0.1932203
## 4 iPad 3+ 136 123 0.1541353 0.1536723
## 5 iPadAir 152 137 0.1716792 0.1717514
## 1 Unknown 97 87 0.1090226 0.1096045
## 6 iPadmini 126 114 0.1428571 0.1423729
## 2 iPad 1 99 89 0.1115288 0.1118644
## 7 iPadmini 2+ 104 94 0.1177945 0.1175141
## accurate.OOB.FALSE accurate.OOB.TRUE max.accuracy.OOB
## 3 33 138 0.8070175
## 4 30 106 0.7794118
## 5 25 127 0.8355263
## 1 21 76 0.7835052
## 6 20 106 0.8412698
## 2 18 81 0.8181818
## 7 17 87 0.8365385dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## All.X.no.rnorm.rf.importance
## startprice.log 100.00000000
## biddable 75.77557376
## idseq.my 61.89274395
## D.ratio.sum.TfIdf.nwrds 6.55080925
## D.TfIdf.sum.stem.stop.Ratio 5.46221930
## color.fctrBlack 5.42672497
## storage.fctr64 4.54591071
## D.ratio.nstopwrds.nwrds 4.48486137
## prdline.my.fctriPadAir 4.21882380
## color.fctrWhite 4.14701866
## prdline.my.fctriPad 1 4.06373906
## D.sum.TfIdf 3.99443151
## condition.fctrNew 3.99149067
## D.TfIdf.sum.post.stem 3.95656358
## storage.fctr16 3.93700840
## color.fctrSpace Gray 3.85738741
## D.TfIdf.sum.post.stop 3.84367131
## cellular.fctr1 3.74587984
## D.nuppr.log 3.68920021
## D.nchrs.log 3.42398279
## prdline.my.fctriPadmini 3.32197629
## prdline.my.fctriPad 3+ 3.25104872
## carrier.fctrAT&T 3.11118805
## cellular.fctrUnknown 3.00719785
## carrier.fctrUnknown 2.90117620
## storage.fctrUnknown 2.86941294
## D.nstopwrds.log 2.72664697
## carrier.fctrVerizon 2.61940946
## D.nwrds.log 2.60132737
## storage.fctr32 2.55032142
## condition.fctrNew other (see details) 2.45350758
## prdline.my.fctriPad 2 2.39999031
## D.T.use 2.25809141
## D.T.condit 2.12215929
## condition.fctrSeller refurbished 2.10269385
## D.nwrds.unq.log 2.04380750
## D.npnct11.log 1.99781559
## D.T.scratch 1.98124045
## prdline.my.fctriPadmini 2+ 1.96047393
## D.terms.n.post.stem 1.82074780
## D.terms.n.post.stop 1.76498655
## D.T.good 1.68403568
## D.T.new 1.65458838
## D.T.screen 1.61414375
## D.npnct13.log 1.54450514
## D.T.ipad 1.51703617
## condition.fctrManufacturer refurbished 1.33277394
## condition.fctrFor parts or not working 1.31102748
## color.fctrGold 1.27778210
## D.T.work 1.23378640
## carrier.fctrSprint 1.01108476
## D.T.great 0.95409170
## D.npnct15.log 0.90427738
## D.ndgts.log 0.81399761
## D.terms.n.stem.stop.Ratio 0.79621742
## D.T.excel 0.78865866
## carrier.fctrT-Mobile 0.66059489
## D.npnct14.log 0.54157235
## D.npnct05.log 0.50191492
## D.npnct01.log 0.44250690
## D.npnct08.log 0.43283592
## D.npnct24.log 0.42904030
## D.npnct12.log 0.40417016
## D.npnct10.log 0.39734986
## D.npnct16.log 0.27323339
## carrier.fctrOther 0.16984240
## D.npnct03.log 0.11963549
## D.P.mini 0.10241921
## D.npnct06.log 0.09382797
## D.P.air 0.06502214
## D.npnct09.log 0.00357743
## D.npnct28.log 0.00000000
## importance Final.rf.importance
## startprice.log 1.000000e+02 1.000000e+02
## biddable 9.822028e+01 9.822028e+01
## idseq.my 6.664811e+01 6.664811e+01
## D.ratio.sum.TfIdf.nwrds 5.501794e+00 5.501794e+00
## D.TfIdf.sum.stem.stop.Ratio 5.470859e+00 5.470859e+00
## color.fctrBlack 4.027819e+00 4.027819e+00
## storage.fctr64 3.854447e+00 3.854447e+00
## D.ratio.nstopwrds.nwrds 4.620204e+00 4.620204e+00
## prdline.my.fctriPadAir 5.122997e+00 5.122997e+00
## color.fctrWhite 4.544942e+00 4.544942e+00
## prdline.my.fctriPad 1 5.076772e+00 5.076772e+00
## D.sum.TfIdf 3.668908e+00 3.668908e+00
## condition.fctrNew 3.633870e+00 3.633870e+00
## D.TfIdf.sum.post.stem 3.853459e+00 3.853459e+00
## storage.fctr16 3.751024e+00 3.751024e+00
## color.fctrSpace Gray 3.280611e+00 3.280611e+00
## D.TfIdf.sum.post.stop 4.540872e+00 4.540872e+00
## cellular.fctr1 4.301726e+00 4.301726e+00
## D.nuppr.log 3.439784e+00 3.439784e+00
## D.nchrs.log 3.818113e+00 3.818113e+00
## prdline.my.fctriPadmini 3.506140e+00 3.506140e+00
## prdline.my.fctriPad 3+ 3.895146e+00 3.895146e+00
## carrier.fctrAT&T 2.242393e+00 2.242393e+00
## cellular.fctrUnknown 2.601186e+00 2.601186e+00
## carrier.fctrUnknown 3.815033e+00 3.815033e+00
## storage.fctrUnknown 2.610476e+00 2.610476e+00
## D.nstopwrds.log 3.451131e+00 3.451131e+00
## carrier.fctrVerizon 2.322727e+00 2.322727e+00
## D.nwrds.log 2.868460e+00 2.868460e+00
## storage.fctr32 2.837569e+00 2.837569e+00
## condition.fctrNew other (see details) 2.235349e+00 2.235349e+00
## prdline.my.fctriPad 2 3.421780e+00 3.421780e+00
## D.T.use 2.239005e+00 2.239005e+00
## D.T.condit 2.226344e+00 2.226344e+00
## condition.fctrSeller refurbished 2.203554e+00 2.203554e+00
## D.nwrds.unq.log 1.943754e+00 1.943754e+00
## D.npnct11.log 1.833161e+00 1.833161e+00
## D.T.scratch 1.917683e+00 1.917683e+00
## prdline.my.fctriPadmini 2+ 2.880925e+00 2.880925e+00
## D.terms.n.post.stem 2.160570e+00 2.160570e+00
## D.terms.n.post.stop 2.005976e+00 2.005976e+00
## D.T.good 1.583586e+00 1.583586e+00
## D.T.new 1.367479e+00 1.367479e+00
## D.T.screen 1.328451e+00 1.328451e+00
## D.npnct13.log 1.402395e+00 1.402395e+00
## D.T.ipad 1.350443e+00 1.350443e+00
## condition.fctrManufacturer refurbished 1.496177e+00 1.496177e+00
## condition.fctrFor parts or not working 1.597595e+00 1.597595e+00
## color.fctrGold 1.376307e+00 1.376307e+00
## D.T.work 1.246854e+00 1.246854e+00
## carrier.fctrSprint 1.046308e+00 1.046308e+00
## D.T.great 1.393428e+00 1.393428e+00
## D.npnct15.log 1.095564e+00 1.095564e+00
## D.ndgts.log 1.116139e+00 1.116139e+00
## D.terms.n.stem.stop.Ratio 5.595316e-01 5.595316e-01
## D.T.excel 1.447960e+00 1.447960e+00
## carrier.fctrT-Mobile 8.766188e-01 8.766188e-01
## D.npnct14.log 3.647107e-01 3.647107e-01
## D.npnct05.log 5.211130e-01 5.211130e-01
## D.npnct01.log 3.115916e-01 3.115916e-01
## D.npnct08.log 4.850993e-01 4.850993e-01
## D.npnct24.log 3.386245e-01 3.386245e-01
## D.npnct12.log 3.027261e-01 3.027261e-01
## D.npnct10.log 2.409854e-01 2.409854e-01
## D.npnct16.log 5.862696e-01 5.862696e-01
## carrier.fctrOther 3.349017e-01 3.349017e-01
## D.npnct03.log 1.677621e-01 1.677621e-01
## D.P.mini 2.438581e-01 2.438581e-01
## D.npnct06.log 3.042029e-01 3.042029e-01
## D.P.air 4.503618e-02 4.503618e-02
## D.npnct09.log 0.000000e+00 0.000000e+00
## D.npnct28.log 1.611318e-03 1.611318e-03print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))##
## N Y
## 530 268# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 184.819 190.592 5.773
## 17 display.session.info 10 0 190.593 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 13.792 64.848 51.056
## 11 fit.models 7 1 93.262 139.697 46.435
## 10 fit.models 7 0 68.280 93.261 24.981
## 14 fit.data.training 8 0 159.191 179.610 20.419
## 12 fit.models 7 2 139.697 155.048 15.351
## 16 predict.data.new 9 0 184.819 190.592 5.773
## 15 fit.data.training 8 1 179.611 184.818 5.207
## 13 fit.models 7 3 155.049 159.191 4.142
## 2 inspect.data 2 0 8.702 12.646 3.944
## 8 select.features 5 0 65.935 67.440 1.505
## 6 cluster.data 4 0 64.848 65.794 0.946
## 9 partition.data.training 6 0 67.441 68.280 0.839
## 3 scrub.data 2 1 12.647 13.347 0.700
## 4 transform.data 2 2 13.348 13.792 0.444
## 1 import.data 1 0 8.320 8.701 0.381
## 7 manage.missing.data 4 1 65.794 65.934 0.141
## duration
## 5 51.056
## 11 46.435
## 10 24.981
## 14 20.419
## 12 15.351
## 16 5.773
## 15 5.207
## 13 4.142
## 2 3.944
## 8 1.505
## 6 0.946
## 9 0.839
## 3 0.700
## 4 0.444
## 1 0.381
## 7 0.140
## [1] "Total Elapsed Time: 190.592 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] C/en_US.UTF-8/C/C/C/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] gdata_2.17.0 randomForest_4.6-10 glmnet_2.0-2
## [4] arm_1.8-6 lme4_1.1-8 Matrix_1.2-2
## [7] MASS_7.3-42 rpart.plot_1.5.2 rpart_4.1-10
## [10] ROCR_1.0-7 gplots_2.17.0 sampling_2.7
## [13] tm_0.6-2 NLP_0.1-8 stringr_1.0.0
## [16] dplyr_0.4.2 plyr_1.8.3 sqldf_0.4-10
## [19] RSQLite_1.0.0 DBI_0.3.1 gsubfn_0.6-6
## [22] proto_0.3-10 reshape2_1.4.1 doMC_1.3.3
## [25] iterators_1.0.7 foreach_1.4.2 doBy_4.5-13
## [28] survival_2.38-3 caret_6.0-47 ggplot2_1.0.1
## [31] lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.11.6 class_7.3-13 gtools_3.5.0
## [4] assertthat_0.1 digest_0.6.8 slam_0.1-32
## [7] R6_2.1.0 BradleyTerry2_1.0-6 chron_2.3-47
## [10] coda_0.17-1 evaluate_0.7 e1071_1.6-4
## [13] lazyeval_0.1.10 minqa_1.2.4 SparseM_1.6
## [16] car_2.0-25 nloptr_1.0.4 rmarkdown_0.7
## [19] labeling_0.3 splines_3.2.1 munsell_0.4.2
## [22] compiler_3.2.1 mgcv_1.8-6 htmltools_0.2.6
## [25] nnet_7.3-10 lpSolve_5.6.11 codetools_0.2-14
## [28] brglm_0.5-9 bitops_1.0-6 nlme_3.1-121
## [31] gtable_0.1.2 magrittr_1.5 formatR_1.2
## [34] scales_0.2.5 KernSmooth_2.23-15 stringi_0.5-5
## [37] RColorBrewer_1.1-2 tools_3.2.1 abind_1.4-3
## [40] pbkrtest_0.4-2 yaml_2.1.13 colorspace_1.2-6
## [43] caTools_1.17.1 knitr_1.10.5 quantreg_5.11